Apparatus and method for recovery of data in a lossy transmission environment

ABSTRACT

A system and method for recovering damaged data in a bistream of encoded data is disclosed. In one embodiment, the encoded data is received and a plurality of candidate decoding are generated. An evaluation measurement is generated for each of the plurality of candidate decoding. A candidate decoding to decode the bitstream of encoded data is selected dependent on the evaluation measurement. In one embodiment, this is used in the transmission of video signals over a potentially lossy communications channel.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.09/016,083, filed Jan. 30, 1998, entitled “Source Coding to Provide forRobust Error Recovery During Transmission Losses”; which is acontinuation-in-part of application Ser. No. 09/002,547, filed Jan. 2,1998, entitled “Image-to-Block Mapping to Provide for Robust ErrorRecovery During Transmission Losses”, application Ser. No. 09/002,470,filed Jan. 2, 1998, entitled “Source Coding to Provide for Robust ErrorRecovery During Transmission Losses”; application Ser. No. 09/002,553,filed Jan. 2, 1998, entitled “Multiple Block Based Recovery Method toProvide for Robust Error Recovery During Transmission Losses”; which arecontinuations-in-part of application Ser. No. 08/956,632, filed Oct. 23,1997, entitled “Image-to-Block Mapping to Provide for Robust ErrorRecovery During Transmission Losses”; application Ser. No. 08/957,555,filed Oct. 23, 1997 entitled “Source Coding to Provide for Robust ErrorRecovery During Transmission Losses”; and application Ser. No.08/956,870, filed Oct. 23, 1997, entitled “Multiple Block Based RecoveryMethod to Provide for Robust Error Recovery During Transmission Losses”.Application Ser. No. 09/016,083, filed Jan. 30, 1998, application Ser.No. 09/002,547, filed Jan. 2, 1998, application Ser. No. 09/002,470,filed Jan. 2, 1998, application Ser. No. 09/002,553, filed Jan. 2, 1998,application Ser. No. 08/956,632, filed Oct. 23, 1997, application Ser.No. 08/957,555, filed Oct. 23, 1997 and application Ser. No. 08/956,870,filed Oct. 23, 1997 are herein incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to providing a robust error recovery dueto data losses incurred during transmission of signals.

2. Art Background

A number of techniques exist for reconstructing lost data due to randomerrors that occur during signal transmission. However, these techniquescannot handle the loss of consecutive packets of data. Consecutive lossof packets of data is described in the art as burst error. Burst errorsresult in a reconstructed signal with such a degraded quality that it iseasily apparent to the end user. Additionally, compression methodologiesused to facilitate high speed communications compound the signaldegradation caused by burst errors, thus adding to the degradation ofthe reconstructed signal. An example of burst error loss affectingtransmitted and/or stored signals is seen in high definition television(“HDTV”) signals and mobile telecommunication applications whereincompression methodologies play an important role.

The advent of HDTV has led to television systems with a much higherresolution than the current standards proposed by the NationalTelevision Systems Committee (“NTSC”). Proposed HDTV signals arepredominantly digital. Accordingly, when a color television signal isconverted for digital use it is common that the luminance andchrominance signals are digitized using eight bits. Digital transmissionof color television requires a nominal bit rate of two hundred andsixteen megabits per second. The transmission rate is greater for HDTVwhich would nominally require about 1200 megabits per second. Such hightransmission rates are well beyond the bandwidths supported by currentwireless standards. Accordingly, an efficient compression methodology isrequired.

Compression methodologies also play an important role in mobiletelecommunication applications. Typically, packets of data arecommunicated between remote terminals in mobile telecommunicationapplications. The limited number of transmission channels in mobilecommunications requires an effective compression methodology prior tothe transmission of packets. A number of compression techniques areavailable to facilitate high transmission rates.

Adaptive Dynamic Range Coding (“ADRC”) and the discrete cosine transform(“DCT”) coding provide image compression techniques known in the art.Both techniques take advantage of the local correlation within an imageto achieve a high compression ratio. However, an efficient compressionalgorithm results in compounded error propagation because errors in anencoded signal are more prominent when subsequently decoded. This errormultiplication results in a degraded video image that is readilyapparent to the user.

SUMMARY OF THE INVENTION

A method for source coding a signal is described. In particular, asignal comprising multiple signal elements is processed. Each signalelement is encoded to form a bitstream. The bits within a givenbitstream are distributed across different bitstreams. Thus, theparameters describing components of the segment elements are distributedacross the different bitstreams. The distributing steps result in errordistribution across multiple levels. Therefore, when the distributingsteps are reversed by the decoder, a burst transmission error becomes adistributed set of localized losses.

Another method is also described for a multiple level shuffling process.A signal is defined as multiple levels wherein each level comprises aplurality of frames, a plurality of pixels, and a plurality of bits. Inone embodiment, shuffling occurs on each level and between levels.Multiple level shuffling causes burst error loss to be distributedacross multiple levels thereby facilitating image reconstruction ofthose areas of the image in which the loss occurred.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects, features and advantages of the present invention will beapparent to one skilled in the art in light of the following detaileddescription in which:

FIG. 1 generally illustrates the processes of signal encoding,transmission, and decoding.

FIG. 2 illustrates one embodiment of a packet structure.

FIG. 3 is a flow diagram illustrating one embodiment of the encodingprocess in accordance with the teachings of the present invention.

FIG. 4 is a flow diagram illustrating one embodiment of the decodingprocess in accordance with the teachings of the present invention.

FIG. 5 illustrates one embodiment of image-to-block mapping inaccordance with the teachings of the present invention.

FIG. 5a illustrates one embodiment of a shuffling pattern used inimage-to-block mapping.

FIG. 6 is an illustration of exemplary complementary and interlockingblock structures.

FIGS. 7a, 7 b, 7 c, 7 d illustrate one embodiment of shuffling patternsfor Y blocks within a frame set.

FIG. 8 is an illustration of one embodiment of cumulative DRdistribution for Buffer 0.

FIG. 8a is an illustration of one embodiment of a partial bufferingprocess in accordance with the teachings of the present invention.

FIG. 9 illustrates one embodiment of the intra buffer YUV blockshuffling process in accordance with the teachings of the presentinvention.

FIG. 10 illustrates one embodiment of the intra group VL-data shufflingprocess in accordance with the teachings of the present invention.

FIG. 11 illustrates one embodiment of Q code concatenation within a3-block group in accordance with the teachings of the present invention.

FIG. 11a illustrates one embodiment of Q code concatenation for framepairs including motion blocks in accordance with the teachings of thepresent invention.

FIG. 12 illustrates one embodiment of pixel data error caused by a ⅙burst error loss.

FIG. 12a illustrates one embodiment of shuffling Q codes anddistributing Q code bits in accordance with the teachings of the presentinvention.

FIG. 12b illustrates one embodiment of pixel data error caused by a ⅙burst error loss of redistributed Q codes.

FIG. 12c illustrates one embodiment of pixel data error caused by a ⅙burst error loss of reassigned Q codes.

FIG. 13 illustrates one embodiment of MIN shuffling in accordance withthe teachings of the present invention.

FIG. 13a illustrates one embodiment of Motion Flag shuffling and of afixed length data loss in one frame pair.

FIG. 14 illustrates one embodiment of a modular shuffling.

FIG. 14a illustrates one embodiment of a modular shuffling result andthe fixed length data loss associated with the modular shuffling.

FIG. 14b illustrates an alternate embodiment of a modular shufflingresult and the fixed length data loss associated with the modularshuffling.

FIG. 14c illustrates an alternate embodiment of a modular shufflingresult and the fixed length data loss associated with the modularshuffling.

FIG. 15 illustrates one embodiment of variable length data buffering ina frame set.

FIG. 16 illustrates one embodiment of inter segment VL-data shuffling inaccordance with the teachings of the present invention.

FIG. 17 is a flow diagram generally illustrating one embodiment of thedata recovery process of the present invention.

FIG. 18 is a flow diagram of one embodiment of the Qbit and Motion Flagrecovery process of the present invention.

FIG. 19 is a table illustrating one embodiment of candidate decodings.

FIGS. 20a, 20 b, 20 c, 20 d illustrate embodiments of measurementsutilized in the Qbit and Motion Flag recovery process of FIG. 18.

FIG. 21 illustrates one embodiment of a table used to determine a squareerror probability function utilized in the Qbit and Motion Flag recoveryprocess of FIG. 18.

FIG. 22 illustrates one embodiment of a Qbit, Motion Flag and auxiliaryinformation recovery process in accordance with the teachings of thepresent invention.

FIG. 23 illustrates the use of a post-amble in one embodiment of abidirectional Qbit and Motion Flag recovery process.

FIGS. 24a, 24 b and 24 c illustrate an alternate embodiment forevaluating candidate decodings.

FIG. 25 illustrates the use of smoothness measures in accordance withthe teachings of one embodiment of the present invention.

FIGS. 26a, 26 b, 26 c, 26 d and 26 e illustrate an alternate embodimentof a process for evaluating candidate decodings.

FIG. 27a illustrates an alternate process for evaluating candidatedecodings and

FIG. 27b illustrates one embodiment for determining weighting values.

DETAILED DESCRIPTION

The present invention provides a method for coding and arranging asignal stream to provide for a robust error recovery. In the followingdescription, for purposes of explanation, numerous details are setforth, in order to provide a thorough understanding of the presentinvention. However, it will be apparent to one skilled in the art thatthese specific details are not required in order to practice the presentinvention. In other instances, well known electrical structures andcircuits are shown in block diagram form in order not to obscure thepresent invention unnecessarily.

The signal processing methods and structures are described from theperspective of one embodiment in which the signals are video signals.However, it is contemplated that the methods and apparatus describedherein are applicable to a variety of types of signals including audiosignals or other digital bitstreams of data, wherein each signal iscomposed of multiple signal elements. Furthermore the embodiment of theprocess described herein utilizes the Adaptive Dynamic Range Coding(“ADRC”) process to compress data; however a variety of codingtechniques and algorithms may be used. For a more detailed discussion onADRC, see “Adaptive Dynamic Range Coding Scheme for Future HDTV DigitalVTR”, Kondo, Fujimori and Nakaya, Fourth International Workshop on HDTVand Beyond, Sep. 4-6, 1991, Turin, Italy.

In the above paper, three different kinds of ADRC are explained. Theseare achieved according to the following equations:

Non-edge-matching ADRC: DR = MAX − MIN + 1$q = \left\lbrack \frac{\left( {x - {MIN} + 0.5} \right) \cdot 2^{Q}}{DR} \right\rbrack$

$x^{\prime} = \left\lbrack {\frac{\left( {q + 0.5} \right) \cdot {DR}}{2^{Q}} + {MIN}} \right\rbrack$Edge-matchingADRC: DR = MAX − MIN$q = \left\lbrack {\frac{\left( {x - {MIN}} \right) \cdot \left( {2^{Q} - 1} \right)}{DR} + 0.5} \right\rbrack$$x^{\prime} = \left\lbrack {\frac{q{\cdot {DR}}}{2^{Q} - 1} + {MIN} + 0.5} \right\rbrack$Multi-stageADRC: DR = MAX − MIN + 1$q = \left\lbrack \frac{\left( {x - {MIN} + 0.5} \right) \cdot 2^{Q}}{DR} \right\rbrack$$\overset{\sim}{x} = \left\lbrack {\frac{\left( {q + 0.5} \right){\cdot {DR}}}{2^{Q}} + {MIN}} \right\rbrack$

Where MAX′ is the averaged value of {tilde over (x)} in the case ofq=2^(Q)−1;

MIN′ is the averaged value of {tilde over (x)} in the case of q=0; andDR^(′) = MAX^(′) − MIN^(′)$q = \left\lbrack {\frac{\left( {x - {MIN}} \right) \cdot \left( {2^{Q} - 1} \right)}{{DR}^{\prime}} + 0.5} \right\rbrack$$\overset{\sim}{x} = \left\lbrack {\frac{q{\cdot {DR}^{\prime}}}{\left( {2^{Q} - 1} \right)} + {MIN}^{\prime} + 0.5} \right\rbrack$

where MAX represents the maximum level of a block, MIN represents theminimum level of a block, x represents the signal level of each sample,Q represents the number of quantization bits, q represents thequantization code (encoded data), {tilde over (x)} represents thedecoded level of each sample, and the square brackets [ ] represent atruncation operation performed on the value within the square brackets.

The signal encoding, transmission, and subsequent decoding processes aregenerally illustrated in FIG. 1. Signal 100 is a data stream input toEncoder 110. Encoder 110 follows the Adaptive Dynamic Range Coding(“ADRC”) compression algorithm and generates Packets 1, . . . N fortransmission along Transmission Media 135. Decoder 120 receives Packets1, . . . N from Transmission Media 135 and generates Signal 130. Signal130 is a reconstruction of Signal 100.

Encoder 110 and Decoder 120 can be implemented a variety of ways toperform the functionality described herein. In one embodiment, Encoder110 and/or Decoder 120 are embodied as software stored on media andexecuted by a general purpose or specifically configured computersystem, typically including a central processing unit, memory and one ormore input/output devices and co-processors. Alternately, the Encoder110 and/or Decoder 120 may be implemented as logic to perform thefunctionality described herein. In addition, Encoder 110 and/or Decoder120 can be implemented as a combination of hardware, software orfirmware.

In the present embodiment Signal 100 is a color video image comprising asequence of video frames, each frame including informationrepresentative of an image in an interlaced video system. Each frame iscomposed of two fields, wherein one field contains data of the evenlines of the image and the other field containing the odd lines of theimage. The data includes pixel values which describe the colorcomponents of a corresponding location in the image. For example, in thepresent embodiment, the color components consist of the luminance signalY, and color difference signals U, and V. It is readily apparent theprocess of the present invention can be applied to signals other thaninterlaced video signals. Furthermore, it is apparent that the presentinvention is not limited to implementations in the Y, U, V color space,but can be applied to images represented in other color spaces.

Referring back to FIG. 1, Encoder 110 divides the Y, U, and V signalsand processes each group of signals independently in accordance with theADRC algorithm. The following description, for purposes of simplifyingthe discussion, describes the processing of the Y signal; however, theencoding steps are replicated for the U and V signals.

In the present embodiment, Encoder 110 groups Y signals across twosubsequent frames, referred to herein as a frame pair, of Signal 100into three dimensional blocks (“3D”) blocks. For one embodiment, a 3Dblock is generated from grouping two 2D blocks from the same localizedarea across a given frame pair, wherein a two dimensional 2D block iscreated by grouping localized pixels within a frame or a field. It iscontemplated that the process described herein can be applied todifferent block structures. The grouping of signals will be furtherdescribed in the image-to-block mapping section below.

Continuing with the present embodiment, for a given 3D block, Encoder110 calculates whether there is a change in pixel values between the 2Dblocks forming the 3D block. A Motion Flag is set if there aresubstantial changes in values. As is known in the art, use of a MotionFlag allows Encoder 110 to reduce the number of quantization codes whenthere is localized image repetition within each frame pair. Encoder 110also detects the maximum pixel intensity value (“MAX”) and the minimumpixel intensity value (“MIN”) within a 3D block. Using values MAX andMIN, Encoder 110 calculates the dynamic range (“DR”) for a given 3Dblock of data. For one embodiment DR=MAX−MIN+1 in the case ofnon-edge-matching ADRC. For edge-matching ADRC, DR=MAX−MIN. In analternative embodiment, Encoder 110 encodes signals on a frame by framebasis for a stream of frames representing a sequence of video frames. Inanother embodiment, Encoder 110 encodes signals on a field by fieldbasis for a stream of fields representing a sequence of video fields.Accordingly, Motion Flags are not used and 2D blocks are used tocalculate the MIN, MAX, and DR values.

In the present embodiment, Encoder 110 references the calculated DRagainst a threshold table (not shown) to determine the number ofquantization bits (“Qbits”) used to encode pixels within the blockcorresponding to the DR. Encoding of a pixel results in a quantizationcode (“Q code”). The Q codes are the relevant compressed image data usedfor storage or transmission purposes.

In one embodiment, the Qbit selection is derived from the DR of a 3Dblock. Accordingly, all pixels within a given 3D block are encoded usingthe same Qbit, resulting in a 3D encoded block. The collection of Qcodes, MIN, Motion Flag, and DR for a 3D encoded block is referred to asa 3D ADRC block. Alternately, 2D blocks are encoded and the collectionof Q codes, MIN, and DR for a given 2D block results in 2D ADRC blocks.

A number of threshold tables can be implemented. In one embodiment, thethreshold table consists of a row of DR threshold values. A Qbitcorresponds to the number of quantization bits used to encode a range ofDR values between two adjacent DRs within a row of the threshold table.In an alternative embodiment, the threshold table includes multiple rowsand selection of a row depends on the desired transmission rate. Eachrow in the threshold table is identified by a threshold index. Adetailed description of one embodiment of threshold selection isdescribed below in the discussion of partial buffering. A furtherdescription of ADRC encoding and buffering is disclosed in U.S. Pat. No.4,722,003 entitled “High Efficiency Coding Apparatus” and U.S. Pat. No.4,845,560 also entitled “High Efficiency Coding Apparatus”, assigned tothe assignee of the present invention.

Here forth the Q codes are referred to as variable length data(“VL-data”). In addition, the DR, MIN, and Motion Flag are referred toas block attributes. The block attributes, together with the thresholdindex, constitute the fixed length data (“FL-data”). Furthermore, inview of the above discussion, the term block attribute describes aparameter associated with a component of a signal element, wherein asignal element includes multiple components.

In an alternate embodiment, the FL-data includes a Qbit code. Theadvantage is that the Qbit information does not have to be derived fromthe DR during the decoding process. Thus, if the DR information is lostor damaged, the Qbit information can still be determined from the Qbitcode. Furthermore, if the Qbit code is lost or damaged, the Qbitinformation can be derived from DR. Thus the requirement to recover theDR and Qbit is reduced.

The disadvantage to including the Qbit code is the additional bits to betransmitted for each ADRC block. However, in one embodiment, Qbit codesfor groups of ADRC blocks are combined, for example, in accordance witha function such as addition or concatenation. For example, if ADRCblocks are grouped in threes and if the Qbit values for each ADRC blockare respectively 3, 4 and 4, the summed value that is encoded into theFL-data is 11. Thus the number of bits required to represent the sum isless than the number of bits required to represent each individual valueand undamaged Qbit values of the group can be used to determine the Qbitvalue without performing a Qbit recovery process such as the onedescribed subsequently.

Other embodiments are also contemplated. For example, Motion Flag datamay also be encoded. A tag with Qbit and Motion Flag data can begenerated and used to reference a table of codes. The configuration andfunction of the coding can vary according to application.

Frames, block attributes, and VL-data describe a variety of componentswithin a video signal. The boundaries, location, and quantity of thesecomponents are dependent on the transmission and compression propertiesof a video signal. In the present embodiment, these components arevaried and shuffled within a bitstream of the video signal to ensure arobust error recovery during transmission losses.

For illustrative purposes, the following description provides for a ⅙consecutive packet transmission loss tolerance, pursuant to an ADRCencoding and shuffling of a video signal. Accordingly, the followingdefinition and division of components exist for one embodiment. Otherembodiments also are contemplated. A data set includes a partition ofdata of a video or other type of data signal. Thus, in one embodiment, aframe set is a type of data set that includes one or more consecutiveframes. A segment includes a memory with the capacity to store aone-sixth division of the Q codes and block attributes included in aframe set. Further, a buffer includes a memory with the capacity tostore a one-sixtieth division of the Q codes and block attributesincluded in a frame set. The shuffling of data is performed byinterchanging components within segments and/or buffers. Subsequently,the data stored in a segment is used to generate packets of data fortransmission. Thus, in the following description if a segment is lostall the packets generated from the segment are lost during transmission.Similarly, if a fraction of a segment is lost then a correspondingnumber of packets generated from the segment are lost duringtransmission.

Although, the following description refers to a ⅙ consecutive packetloss for data encoded using ADRC encoding, it is contemplated that themethods and apparatus described herein are applicable to a design of a1/n consecutive packets loss tolerance coupled to a variety ofencoding/decoding schemes.

FIG. 2 illustrates one embodiment of Packet Structure 200 used for thetransmission of the data across point-to-point connections as well asnetworks. Packet Structure 200 is generated by Encoder 110 and istransmitted across Transmission Media 135. For one embodiment, PacketStructure 200 comprises five bytes of header information, eight DR bits,eight MIN bits, a Motion Flag bit, a five bit threshold index, and 354bits of Q codes. The packet structure described herein is illustrativeand may typically be implemented for transmission in an asynchronoustransfer mode (“ATM”) network. However, the present invention is notlimited to the packet structure described and a variety of packetstructures that are used in a variety of networks can be utilized.

As noted earlier, Transmission Media (e.g., media) 135 is not assumed toprovide error-free transmission and therefore packets may be lost ordamaged. As noted earlier, conventional methods exist for detecting suchloss or damage, but substantial image degradation will generally occur.The system and methods of the present invention therefore teach sourcecoding to provide robust recovery from such loss or damage. It isassumed throughout the following discussion that a burst loss, that isthe loss of several consecutive packets, is the most probable form oferror, but some random packet losses might also occur.

To ensure a robust recovery for the loss of one or more consecutivepackets of data, the system and methods of the present invention providemultiple level shuffling. In particular, the FL-data and the VL-dataincluded in a transmitted packet comprise data from spatially andtemporally disjointed locations of an image. Shuffling data ensures thatany burst error is scattered and facilitates error recovery. As will bedescribed below, the shuffling allows recovery of block attributes andQbit values.

Data Encoding/Decoding

FIG. 3 is a flow diagram illustrating one embodiment of the encodingprocess performed by Encoder 110. FIG. 3 further describes an overviewof the shuffling process used to ensure against image degradation and tofacilitate a robust error recovery.

In step one of FIG. 3, an input frame set, also referred to as a displaycomponent, is decimated to reduce the transmission requirements. The Ysignal is decimated horizontally to three-quarters of its original widthand the U and V signals are each decimated to one-half of their originalheight and one-half of their original width. This results in a 3:1:0video format with 3960 Y blocks, 660 U blocks and 660 V blocks in eachframe pair. As noted earlier, the discussion will describe theprocessing of Y signals; however, the process is applicable to the U andV signals. At step two, the two Y frame images are mapped to 3D blocks.At step three, 3D blocks are shuffled. At step four, ADRC buffering andencoding is used. At step five, encoded Y, U and V blocks are shuffledwithin a buffer.

At step six, the VL-data for a group of encoded 3D blocks and theircorresponding block attributes are shuffled. At step seven, the FL-datais shuffled across different segments. At step eight, post-amble fillingis performed in which variable space at the end of a buffer is filledwith a predetermined bitstream. At step nine, the VL-data is shuffledacross different segments.

For illustrative purposes the following shuffling description provides amethod for manipulation of pixel data before and after encoding. For analternative embodiment, independent data values are shuffled/deshuffledvia hardware. In particular, the hardware maps the address of blockvalues to different addresses to implement the shuffling/deshufflingprocess. However, address mapping is not possible for data dependentvalues because shuffling has to follow the processing of data. The intragroup VL-data shuffling described below includes the data dependentvalues. Further, for illustrative purposes the following shufflingdescription occurs on discrete sets of data. However, for alternativeembodiments a signal is defined based on multiple data levels rangingfrom bits, to pixels, and to frames. Shuffling is possible for eachlevel defined in the signal and across different data levels of thesignal.

FIG. 4 is a flow diagram illustrating one embodiment of decoding processperformed by Decoder 120. Preferably, the conversion and deshufflingprocesses are the inverse of the processes represented in FIG. 3. FIG.4, further describes, in different combinations of Qbit, Motion Flag,DR, MIN and pixel data, an innovative process for error recovery. Theerror recovery process is described below in different combinations fordifferent embodiments, Qbit, Motion Flag, DR, MIN and pixel recovery.

Image-to-Block Mapping

In the present embodiment, a single frame typically comprises 5280 2Dblocks wherein each 2D block comprises 64 pixels. Thus, a frame paircomprises 5280 3D blocks as a 2D block from a first frame and a 2D blockfrom a subsequent frame are collected to form a 3D block.

Image-to-block mapping is performed for the purpose of dividing a frameor frame set of data into 2D blocks or 3D blocks respectively. Moreover,image-to-block mapping includes using a complementary and/orinterlocking pattern to divide pixels in a frame to facilitate robusterror recovery during transmission losses. However, to improve theprobability that a given DR value is not too large, each 2D block isconstructed from pixels in a localized area.

FIG. 5 illustrates one embodiment of an image-to-block mapping processfor an exemplary 16 pixel section of an image. Image 500 comprises 16pixels forming a localized area of a single frame. Each pixel in Image500 is represented by an intensity value. For example, the pixel in thetop left hand side of the image has an intensity value equal to 100whereas the pixel in the bottom right hand side of the image has anintensity value of 10.

In one embodiment, pixels from different areas of Image 500 are used tocreate 2D Blocks 510, 520, 530, and 540. 2D Blocks 510, 520, 530, and540 are encoded, shuffled (as illustrated below), and transmitted.Subsequent to transmission, 2D Blocks 510, 520, 530, and 540 arerecombined and used to form Image 550. Image 550 is a reconstruction ofImage 500.

To ensure accurate representation of Image 500 despite a possibletransmission loss, FIG. 5 is an interlocking complementary blockstructure, one embodiment of which is illustrated in FIG. 5, is used toreconstruct Image 550. In particular, the pixel selection used to create2D Blocks 510, 520,530, and 540 ensures that a complementary and/orinterlocking pattern is used to recombine the blocks when Image 550 isreconstructed. Accordingly, when a particular 2D block's attribute islost during transmission, contiguous sections of Image 550 are notdistorted during reconstruction. For example, as illustrated in FIG. 5the DR of 2D Block 540 is lost during data transmission. However, duringreconstruction of Image 550, the decoder utilizes multiple neighboringpixels of neighboring blocks through which a DR can be recovered for themissing DR of 2D Block 540. In addition, as will be subsequentlydescribed, the combination of complementary patterns and shiftingincreases the number of neighboring pixels, preferably maximizing thenumber of neighboring pixels that originate from other blocks,significantly improving DR and MIN recovery.

FIG. 5a illustrates one embodiment of a shuffling pattern used to form2D blocks in one embodiment of the image-to-block mapping process. Animage is decomposed into two sub-images, Sub-Image 560 and Sub-Image570, based on alternating pixels. Rectangular shapes are formed inSub-Image 560 to delineate the 2D block boundaries. For purposes ofdiscussion, the 2D blocks are numbered 0, 2,4, 7,9, 11, 12, 14, 16, 19,21, and 23. Tile 565 illustrates the pixel distribution for a 2D blockwithin Sub-Image 560.

In Sub-Image 570, the 2D block assignment is shifted by eight pixelshorizontally and four pixels vertically. This results in a wrap around2D block assignment and overlap when Sub-Images 560 and 570 are combinedduring reconstruction. The 2D blocks are numbered 1, 3, 5, 6, 8, 10, 13,15, 17, 18, 20, and 22. Tile 575 illustrates the pixel distribution fora 2D block within Sub-Image 570. Tile 575 is the complementary structureof Tile 565. Accordingly, when a particular block's attribute is lostduring transmission, neighboring pixels through which a block attributecan be recovered for the missing 2D block exists. Additionally, anoverlapping 2D block of pixels with a similar set of block attributesexist. Therefore, during reconstruction of the image the decoder hasmultiple neighboring pixels from adjacent 2D blocks through which a lostblock attribute can be recovered.

FIG. 6 illustrates other complementary and interlocking 2D blockstructures. Other structures may also be utilized. Similar to FIG. 5,these 2D block structures illustrated in FIG. 6, ensure surrounding 2Dblocks are present despite transmission losses for a given 2D block.However, Patterns 610 a, 610 b, and 610 d use horizontal and/or verticalshifting during the mapping of pixels to subsequent 2D blocks.Horizontal shifting describes shifting the tile structure in thehorizontal direction a predetermined number of pixels prior to beginninga new 2D block boundary. Vertical shifting describes shifting the tilestructure in the vertical direction a predetermined number of pixelsprior to beginning a new 2D block boundary. In application, horizontalshifting only may be applied, vertical shifting may only be applied, ora combination of horizontal and vertical shifting may be applied.

Pattern 610 a illustrates a spiral pattern used for image-to-blockmapping. The spiral pattern follows a horizontal shifting to createsubsequent 2D blocks during the image-to-block mapping process. Patterns610 b and 610 d illustrate complementary patterns wherein pixelselection is moved by a horizontal and vertical shifting to createsubsequent 2D blocks during the image-to-block mapping process. Further,Patterns 610 b and 610 d illustrate alternating offsets on pixelsselection between 2D blocks. Pattern 610 c illustrates using anirregular sampling of pixels to create a 2D block for image-to-blockmapping. Accordingly, the image-to-block mapping follows any mappingstructure provided a pixel is mapped to a 2D block only once.

FIG. 5, FIG. 5a and FIG. 6 describe image-to-block mapping for 2D blockgeneration. It is readily apparent that the processes are applicable to3D blocks. As described above, 3D block generation follows the sameboundary definition as a 2D block, however the boundary division extendsacross a subsequent frame resulting in a 3D block. In particular, a 3Dblock is created by collecting the pixels used to define a 2D block in afirst frame together with pixels from a 2D block in a subsequent frame.In one embodiment, both pixels in the 2D block from the first frame andthe 2D block from the subsequent frame are from the exact same location.

Intra Frame Set Block Shuffling

The pixels values for a given image are closely related for a localizedarea. However, in another area of the same images the pixel values mayhave significantly different values. Thus, subsequent to encoding the DRand MIN values for spatially close 2D or 3D blocks in a section of animage have similar values, whereas the DR and MIN values for blocks inanother section of the image may be significantly different.Accordingly, when buffers are sequentially filled with encoded data fromspatially close 2D or 3D blocks of an image, a disproportionate usage ofbuffer space occurs. Intra frame set block shuffling occurs prior toADRC encoding and includes shuffling the 2D or 3D blocks generatedduring the image-to-block mapping process. This shuffling processensures an equalized buffer usage during a subsequent ADRC encoding.

FIGS. 7a-7 d illustrate one embodiment of shuffling 3D Y-blocks. The 3DY-blocks in FIGS. 7a-7 d are generated from applying the image-to-blockmapping process described above to a frame pair containing only Ysignals. The 3D Y-blocks are shuffled to ensure that the buffers used tostore the encoded frame pair contain 3D Y-blocks from different parts ofthe frame pair. This leads to similar DR distribution during ADRCencoding. A similar DR distribution within each buffer leads toconsistent buffer utilization.

FIGS. 7a-7 d also illustrate 3D block shuffling using physicallydisjointed 3D blocks to ensure that transmission loss of consecutivepackets results in damaged block attributes scattered across the image,as opposed to a localized area of the image.

The block shuffling is designed to widely distribute block attributes inthe event of small, medium, or large, burst packet losses occur. In thepresent embodiment, a small burst loss is thought of as one where a fewpackets are lost; a medium loss is one in which the amount of data thatcan be held in one buffer is lost; and a large loss is one in which theamount of data that can be held in one segment is lost. During the 3Dblock shuffling each group of three adjacent blocks are selected fromrelatively remote parts of the image. Accordingly, during the subsequentintra group VL-data shuffling (to be detailed later), each group isformed from 3D blocks that have differing statistical characteristics.Distributed block attribute losses allow for a robust error recoverybecause a damaged 3D block is surrounded by undamaged 3D blocks and theundamaged 3D blocks can be used to recover lost data.

FIG. 7a illustrates a frame pair containing 66 3D Y-blocks in thehorizontal direction and 60 3D Y-blocks in the vertical direction. The3D Y-blocks are allocated into Segments 0-5. As illustrated, the 3DY-block assignment follows a two by three column section such that one3D Y-block from each section is associated with a segment. Thus, if nofurther shuffling is performed and a burst loss of the first 880 packetsoccurs, all the block attributes associated with Segment 0 are lost.However, as later described, FL-data shuffling is performed to furtherdisperse block attribute losses.

FIG. 7b illustrates the scanning order of 3D Y-blocks numbered “0” usedto enter into Segment 0. Each “0” 3D Y-block of FIG. 7a is numbered 0,1, 2, 3, . . . 659 to illustrate their location in the stream that isinputted into Segment 0. Using the block numbering to allocate segmentassignment the remaining 3D Y-blocks are inputted into Segments 1-5,thus resulting in a frame pair shuffled across multiple segments.

FIG. 7c illustrates the 660 3D Y-blocks comprising one segment. The 3DY-blocks numbered 0-65 are inputted into Buffer 0. Similarly the 3DY-blocks adjacent to the numbered 3D Y-blocks are inputted intoBuffer 1. The process is repeated to fill Buffers 2-9. Accordingly,damage to a buffer during data transmission results in missing 3DY-blocks from different parts of the image.

FIG. 7d illustrates the final ordering of the “0” 3D Y-blocks across abuffer. 3D Y-blocks 0, 1, and 2 occupy the first three positions in thebuffer. The process is repeated for the rest of the buffer. Accordingly,loss of three 3D Y-blocks during data transmission results in missing 3DY-blocks from distant locations within the image.

FIGS. 7a-d illustrate one embodiment of 3D block distributions for 3DY-blocks of a frame set. In alternative embodiments, however, 3D blockdistributions for 3D U-blocks and 3D V-blocks are available. The 3DU-blocks are generated from applying the image-to-block mapping process,described above, to a frame set containing only U signals. Similarly, 3DV-blocks are generated from applying the image-to-block mapping processto a frame set containing only V signals. Both the 3D U-block and the 3DV-block follow the 3D Y-block distribution described above. However, aspreviously described, the number of 3D U-blocks and 3D V-blocks eachhave a 1:6 proportion to 3D Y-blocks.

FIGS. 7a-d are used to illustrate one embodiment of intra frame setblock shuffling for a Y signal such that burst error of up to ⅙ of thepackets lost during transmission is tolerated and further ensuresequalized buffer use. It will be appreciated by one skilled in the artthat segment, buffer, and ADRC block assignments can be varied to ensureagainst 1/n burst error loss or to modify buffer utilization.

Partial Buffering

As illustrated in FIG. 3, the ADRC encoding and buffering processesoccur in step four. Dependent on the encoding technique, 2D or 3D blocksgenerated during the image-to-block mapping process are encodedresulting in 2D or 3D ADRC blocks. A 3D ADRC block, contains Q codes, aMIN value, a Motion Flag, and a DR. Similarly, a 2D ADRC block containsQ codes, a MIN, and a DR. A 2D ADRC block, however, does not include aMotion Flag because the encoding is performed on a single frame or asingle field.

A number of buffering techniques are found in the prior art (see forexample, High Efficiency Coding Apparatus, U.S. Pat. No. 4,845,560 ofKondo et. al. and High Efficiency Coding Apparatus, U.S. Pat. No.4,722,003 of Kondo). Both High Efficiency Coding Apparatus patents arehereby incorporated by reference.

The partial buffering process set forth below, describes an innovativemethod for determining the encoding bits used in ADRC encoding. Inparticular, partial buffering describes a method of selecting thresholdvalues from a threshold table designed to provide a constanttransmission rate between remote terminals while restricting errorpropagation. In an alternative embodiment, the threshold table isfurther designed to provide maximum buffer utilization. In oneembodiment, a buffer is a memory that stores a one-sixtieth division ofencoded data from a given frame set. The threshold values are used todetermine the number of Qbits used to encode the pixels in 2D or 3Dblocks generated from the image-to-block mapping process previouslydescribed.

The threshold table includes rows of threshold values, also referred toas a threshold set, and each row in the threshold table is indexed by athreshold index. In one embodiment, the threshold table is organizedwith threshold sets that generate a higher number of Q code bits locatedin the upper rows of the threshold table. Accordingly, for a givenbuffer having a predetermined number of bits available, Encoder 110moves down the threshold table until a threshold set that generates lessthan a predetermined number of bits is encountered. The appropriatethreshold values are used to encode the pixel data in the buffer.

In one embodiment, a transmission rate of no more than 30 Mbps isdesired. The desired transmission rate results in 31,152 bits availablefor VL-data storage in any given buffer. Accordingly, for each buffer acumulative DR distribution is computed and a threshold set is selectedfrom the threshold table to encode the pixels in 3D or 2D blocks intoVL-data.

FIG. 8 illustrates one embodiment of selected threshold values and theDR distribution for Buffer 0. The vertical axis of FIG. 8 includes thecumulative DR distribution. For example, the value “b” is equal to thenumber of 3D or 2D blocks whose DR is greater than or equal to L₃. Thehorizontal axis includes the possible DR values. In one embodiment, DRvalues range from 0 to 255. Threshold values L₄, L₃, L₂, and L₁ describea threshold set used to determine the encoding of a buffer.

In one embodiment, all blocks stored in Buffer 0 are encoded usingthreshold values L₄, L₃, L₂, and L₁. Accordingly, blocks with DR valuesgreater than L₄ have their pixel values encoded using four bits.Similarly, all pixels belonging to blocks with DR values between L₃ andL₄ are encoded using three bits. All pixels belonging to blocks with DRvalues between L₂ and L₃ are encoded using two bits. All pixelsbelonging to blocks with DR values between L₁ and L₂ are encoded usingone bit. Finally, all pixels belonging to blocks with DR values smallerthan L₁ are encoded using zero bits. L₄, L₃, L₂, and L₁ are selectedsuch that the total number of bits used to encode all the blocks inBuffer 0 is as close as possible to a limit of 31,152 bits withoutexceeding the limit of 31,152.

FIG. 8a illustrates the use of partial buffering in one embodiment.Frame 800 is encoded and stored in Buffers 0-59. Provided a transmissionerror inhibits data recovery, the decoding process is stalled for Frame800 until error recovery is performed on the lost data. However, partialbuffering restricts the error propagation within a buffer, thus allowingdecoding of the remaining buffers. In one embodiment, a transmissionerror inhibits the Qbit and Motion Flag recovery for Block 80 in Buffer0. Partial buffering limits the error propagation to the remainingblocks within Buffer 0. Error propagation is limited to Buffer 0 becausethe end of Buffer 0 and the beginning of Buffer 1 are known due to thefixed buffer length. Accordingly, Decoder 120 can begin processing ofblocks within Buffer 1 without delay. Additionally, the use of differentthreshold sets to encode different buffers allows Encoder 110 tomaximize/control the number of Q codes bits included in a given buffer,thus allowing a higher compression ratio. Furthermore, the partialbuffering process allows for a constant transmission rate becauseBuffers 0-59 consist of a fixed length.

In one embodiment, a buffer's variable space is not completely filledwith Q code bits because a limited number of threshold sets exist.Accordingly, the remaining bits in the fixed length buffer are filledwith a predetermined bitstream pattern referred to as a post-amble. Aswill be described subsequently, the post-amble enables bidirectionaldata recovery because the post-amble delineates the end of the VL-dataprior to the end of the buffer.

Intra Buffer YUV Block Shuffling

Y, U, and V, signals each have unique statistical properties. To improvethe Qbit and Motion Flag recovery process (described below) the Y, U,and V signals are multiplexed within a buffer. Accordingly, transmissionloss does not have a substantial effect on a specific signal.

FIG. 9 illustrates one embodiment of the intra buffer YUV blockshuffling process in which YUV ADRC blocks are derived from the Y, U,and V signals respectively. Buffer 900 illustrates the ADRC blockassignments after intra frame set block shuffling. Buffer 900 comprises66 Y-ADRC blocks followed by 11 U-ADRC blocks which are in turn followedby 11 V-ADRC blocks. Buffer 910 shows the YUV ADRC block organizationafter intra buffer YUV block shuffling. As illustrated, three Y-ADRCblocks are followed by a U-ADRC block or three Y-ADRC blocks arefollowed by a V-ADRC block. Intra buffer YUV block shuffling reducessimilarity between adjacent block's bitstreams within the buffer.Alternative embodiments of intra buffer YUV block shuffling with adifferent signal, i.e., YUV ratios or other color spaces are possibledependent on the initial image format.

Intra Group VL-Data Shuffling

Intra group VL-data shuffling comprises three processing steps. Thethree processing steps include Q code concatenation, Q codereassignment, and randomizing concatenated Q codes. FIG. 10 illustratesone embodiment of intra group VL-data shuffling wherein three processingsteps are applied consecutively to Q codes stored in a buffer. Inalternative embodiments, a subset of the processing step is applied inintra group VL-data shuffling. Each processing step independentlyassists in the error recovery of data lost during transmission.Accordingly, each processing step is described independently. A detaileddescription of error recovery is provided below in the discussion ofdata recovery.

1. Q Code Concatenation

Q code concatenation ensures that groups of ADRC blocks are decodedtogether. Group decoding facilitates error recovery because additionalinformation is available from neighboring blocks during the datarecovery process detailed below. For one embodiment, Q codeconcatenation is applied independently to each group of three ADRCblocks stored in a buffer. In an alternative embodiment, a groupincludes ADRC block(s) from different buffers. The concatenation of Qcodes across three ADRC blocks is described as generating oneconcatenated ADRC tile. FIG. 11 and FIG. 11a illustrate one embodimentof generating concatenated ADRC tiles.

FIG. 11 illustrates one embodiment of generating a concatenated ADRCtile from 2D ADRC blocks. Specifically, the concatenation is performedfor each Q code (q₀-q₆₃) included in 2D ADRC Blocks 0, 1, and 2resulting in the sixty four Q codes of Concatenated ADRC Tile A. Forexample, the first Q code q_(0,0) (0th quantized value) of 2D ADRC Block0 is concatenated to the first Q code q_(0,1) of 2D ADRC Block 1. Thetwo concatenated Q codes are in turn concatenated to the first Q codeq_(0,2) of 2D ADRC Block 2, thus resulting in Q₀ of Concatenated ADRCTile A. The processes is repeated until Q₆₃ is generated. Alternatively,the generation of Q_(i) in Concatenated ADRC Tile A is described by theequation

Q _(i) =[q _(i,0) ,q _(i,1) ,q _(i,2)]

i=0,1,2, . . . 63

Additionally, associated with each Q_(i) in Concatenated ADRC Tile Athere is a corresponding number of N bits that represents the totalnumber of bits concatenated to generate a single Q_(i).

FIG. 11a illustrates one embodiment of generating a concatenated ADRCtile from frame pairs including motion blocks. A motion block is a 3DADRC block with a set Motion Flag. The Motion Flag is set when apredetermined number of pixels within two 2D blocks structure created byimage-to-block mapping process described earlier, change in valuebetween a first frame and a subsequent frame. In an alternativeembodiment, the Motion Flag is set when the maximum value of each pixelchange between the 2D block of a first frame and a subsequent frameexceeds a predetermined value. In contrast, non-motion (i.e.,stationary) block includes a 3D ADRC block with a Motion Flag that isnot set. The Motion Flag remains un-set when a predetermined number ofpixels within the two 2D blocks of a first frame and a subsequent framedo not change in value. In an alternative embodiment, the Motion Flagremains un-set when the maximum value of each pixel change between afirst frame and a subsequent frame does not exceed a predeterminedvalue.

A motion block includes Q codes from an encoded 2D block in a firstframe and an encoded 2D block in a subsequent frame. The collection of Qcodes corresponding to a single encoded 2D block are referred to as anADRC tile. Accordingly, a motion block generates two ADRC tiles.However, due to the lack of motion, a stationary block need only includeone-half of the number of Q codes of a motion block, thus generatingonly one ADRC tile. In the present embodiment, the Q codes of astationary block are generated by averaging corresponding pixels valuesbetween a 2D block in a first frame and a corresponding 2D block in asubsequent frame. Each averaged pixel value is subsequently encodedresulting in the collection of Q codes forming a single ADRC tile.Accordingly, Motion Blocks 1110 and 1130 generate ADRC Tiles 0, 1, 3,and 4. Stationary Block 1120 generates ADRC Tile 2.

The concatenated ADRC tile generation of FIG. 11a concatenates the Qcodes for ADRC Tiles 0-4 into Concatenated ADRC Tile B. Specifically,the concatenation is performed for each Q code (q₀-q₆₃) included in ADRCTiles 0, 1, 2, 3 and 4 resulting in the sixty four Q codes ofConcatenated ADRC Tile B. Alternatively, the generation of each Q code,Q_(i), in Concatenated ADRC Tile B is described by the mathematicalequation

Q _(i) =[q _(i,0) ,q _(i,1) ,q _(i,2) ,q _(i,3) ,q _(i,4)]

i=0, 1, 2, . . . 63

2. Q Code Reassignment

Q code reassignment ensures that bit errors caused by transmissionlosses are localized within spatially disjointed pixels. In particular,during Q code reassignment, Q codes are redistributed and the bits ofthe redistributed Q codes are shuffled. Accordingly, Q code reassignmentfacilitates error recovery because undamaged pixels surround eachdamaged pixel. Furthermore, DR and MIN recovery is aided because pixeldamage is distributed evenly throughout an ADRC block, DR and MINrecovery is detailed below in the data recovery discussion.

FIG. 12 illustrates one embodiment of pixel corruption during thetransmission loss of a ⅙ burst error loss. In particular, 2D ADRC Blocks1210, 1220, and 1230 each include sixty four pixels encoded using threebits. Accordingly, each pixel, P₀ through P₆₃, of a 2D ADRC block isrepresented by three bits. 2D ADRC Block 1210 shows the bit losspattern, indicated by a darkened square, of bits when the first bit ofevery six bits are lost. Similarly, the bit loss pattern when the secondbit or fourth bit of every six bits are lost are shown in 2D ADRC Blocks1220 and 1230, respectively. FIG. 12illustrates that without Q codereassignment one-half of all the pixels 2D ADRC Blocks 1210, 1220, and1230 are corrupted for a ⅙ burst error loss.

For one embodiment, Q code reassignment is applied independently to eachconcatenated ADRC tile stored in a buffer, thus ensuring that bit errorsare localized within spatially disjointed pixels upon deshuffling. In analternative embodiment, Q code reassignment is applied to each ADRCblock stored in a buffer.

FIG. 12a illustrates one embodiment of Q code reassignment thatgenerates a bitstream of shuffled Q code bits from a concatenated ADRCtile. Table 122 and Table 132 illustrate the Q code redistribution.Bitstreams 130 and 140 illustrate the shuffling of Q code bits.

Table 122 shows the concatenated Q codes for Concatenated ADRC Tile A.Q₀ is the first concatenated Q code and Q₆₃ is the final concatenated Qcode. Table 132 illustrates the redistribution of Q codes. For oneembodiment Q₀, Q₆, Q₁₂, Q₁₈, Q₂₄, Q₃₀, Q₃₆, Q₄₂, Q₄₈, Q₅₄, and Q₆₀ areincluded in a first set, partition 0. Following Table 132, the followingeleven concatenated Q codes are included in partition 1. The steps arerepeated for partitions 2-5. The boundary of a partition is delineatedby a vertical line in Table 132. This disjointed spatial assignment ofconcatenated Q codes to six partitions ensures that a ⅙ burst error lossresults in a bit loss pattern distributed across a group of consecutivepixels.

FIG. 12b illustrates one embodiment of the bit pattern loss created bythe ⅙ burst error loss of redistributed Q codes. In particular, 2D ADRCblocks 1215, 1225, and 1235 each include sixty four pixels encoded usingthree bits. Accordingly, each pixel P₀ through P₆₃, of each 2D ADRCblock, is represented by three bits. In 2D ADRC Blocks 1215, 1225, and1235 the bit loss pattern, indicated by a darkened square, is localizedacross a group of consecutive pixels. Accordingly, only elevenconsecutive pixels within each 2D ADRC Block 1215, 1225, and 1235 arecorrupted for a given segment loss. In an alternative embodiment, Q codeassignment to partitions include Q codes from different motion blocks,thus providing both a disjointed spatial and temporal assignment of Qcodes to six segments. This results in additional undamagedspatial-temporal pixels during a ⅙ burst error loss and furtherfacilitates a more robust error recovery.

Referring to FIG. 12a, the bits of the redistributed Q codes in Table132 are shuffled across a generated bitstream so that adjacent bits inthe bitstream are from adjacent partitions. The Q code bits for all thepartitions in Table 132 are concatenated into Bitstream 130. For a givenpartition adjacent bits in Bitstream 130 are scattered to every sixthbit location in the generated Bitstream 140. Accordingly, bits numberzero through five, of Bitstream 140, include the first bit from thefirst Q code in each partition. Similarly, bits number six througheleven, of Bitstream 140, include the second bit from thefirst Q code ineach partition. The process is repeated for all Q code bits.Accordingly, a ⅙ burst error loss will result in a spatially disjointedpixel loss.

FIG. 12c illustrates one embodiment of the bit pattern loss created bythe ⅙ burst error loss of reassigned (i.e. redistributed and shuffled) Qcodes. In particular, 2D ADRC Blocks 1217, 1227, and 1237 each includesixty four pixels encoded using three bits. Accordingly, each pixel P₀through P₆₃, of each 2D ADRC Block, is represented by three bits. In 2DADRC Blocks 1217, 1227, and 1237, the bit loss pattern, indicated by adarkened square, is distributed across spatially disjointed pixels, thusfacilitating pixel error recovery.

3. Randomization of Q codes bits

The Q code bits are randomized using a masking key to assist the decoderin recovering lost and damaged data. In particular, during encoding akey, denoted by KEY, is used to mask a bitstream of Q codes.Accordingly, the decoder must discern the correct values of KEY tounmask the bitstream of Q codes.

In one embodiment, KEY is used to mask a bitstream of Q codes generatedby the Q code reassignment of three ADRC blocks. As previouslydescribed,an ADRC block includes FL-data and Q codes. Each key element (“d_(i)”)of the masking key is generated by the combination of the FL-data valuesand the number of quantization bits (“q_(i)”) associated with acorresponding ADRC block. In one embodiment, Motion Flags and Qbits areused to define a key. Accordingly, in this embodiment, the value of akey element is generated from the mathematical equation

d _(i)=5·m _(i) +q _(i)

where i=0,1,2 and q_(i)=0,1,2,3,4

The variable m_(i) equals the Motion Flag. Accordingly, when thecorresponding ADRC block is a stationary block, m_(i) equals 0 and whenthe corresponding ADRC block is a motion block, m_(i) equals 1.Furthermore, the variable q_(i) represents the quantization bits used toencode the corresponding ADRC block. Accordingly, q_(i) has a value of0, 1, 2,3, or 4 for a four bit ADRC encoding technique. In oneembodiment, KEY for a group of three ADRC blocks is defined with threekey elements (“d_(i)”) according to the following equation:

KEY=d ₀+10•d ₁+100•d ₂

Thus, during the recovery of Motion Flag or Qbit data possible keyvalues are regenerated depending on the values used to create themasking keys. The regenerated key values are used to unmask the receivedbitstream of Q codes resulting in candidate decodings. A detaileddescription of regenerating key values and the selection of a specificcandidate decoding is provided below in the discussion of data recovery.

In an alternative embodiments, the masking key is generated form avariety of elements. Thus, providing the decoder with the specificinformation relating to an element without having to transmit theelement across a transmission media. In one embodiment, DR or MIN valuescorresponding to an ADRC block are used to generate a masking key tomask the bitstream representing the ADRC block.

FIGS. 10-12 illustrate intra group VL-data shuffling tolerated up to ⅙packet data loss during transmission. It will be appreciated by oneskilled in the art, that the number of total partitions and bitseparation can be varied to ensure against 1/n burst error loss.

Inter Segment FL-Data Shuffling

Inter segment FL-data shuffling describes rearranging block attributesamong different segments. Rearranging block attributes provides for adistributed loss of data. In particular, when FL-data from a segment islost during transmission the DR value, MIN value, and Motion Flag valuelost do not belong to the same block. FIGS. 13 and 14 illustrate oneembodiment of inter segment FL-data shuffling.

FIG. 13 illustrates the contents of Segments 0 to 5. For one embodiment,each segment comprises 880 DRs, 880 MINs, 880 Motion Flags, and VL-datacorresponding to 660 Y-blocks, 110 U-blocks, and 110 V-blocks. Asillustrated in graph MIN Shuffling 1300, the MIN values for Segment 0are moved to Segment 2, the MIN values for Segment 2 are moved toSegment 4, and the MIN values for Segment 4 are moved to Segment 0.Additionally, the MIN values for Segment 1 are moved to Segment 3, theMIN values for Segment 3 are moved to Segment 5, and the Motion Flagvalues for Segment 5 are moved to Segment 1.

FIG. 13a illustrates Motion Flag shuffling. As illustrated, in graphMotion Flag Shuffling 1305, the Motion Flag values for Segment 0 aremoved to Segment 4, the Motion Flag values for Segment 2 are moved toSegment 0, and the Motion Flag values for Segment 4 are moved to Segment2.

Additionally, the Motion Flag values for Segment 1 are moved to Segment5, the Motion Flag values for Segment 3. are moved to Segment 1, and theMotion Flag values for Segment 5 are moved to Segment 3. Loss pattern1310 illustrates the FL-data loss after Segment 0 is lost duringtransmission.

For a specific block attribute, both FIG. 13 and FIG. 13a illustrateshuffling all instances of the specific block attribute betweensegments. For example, in FIG. 13 the 880 MIN values from Segment 0 arecollectively exchanged with the 880 MIN values in Segment 2. Similarly,in FIG. 13a the 880 Motion Flags for Segment 0 are collectivelyexchanged with the 880 Motion Flags in Segment 4. During a transmissionloss of consecutive packets, this collective shuffling of blockattributes results in a disproportionate loss of a specific blockattributes for a block group. In one embodiment, a block group includesthree ADRC blocks.

FIG. 14 illustrates one embodiment of a modular three shuffling processfor DR, MIN, and Motion Flag values. A modular three shuffling describesa shuffling pattern shared across three blocks (i.e., a block group) inthree different segments. The shuffling pattern is repeated for allblock groups within the three different segments. However, a differentshuffling pattern is used for different block attributes. Accordingly,the modular three shuffling process distributes block attributes overall three segments. In particular, for a given block group a modularthree shuffling ensures that only one instance of a specific blockattribute is lost during the transmission loss of a segment. Thus,during the data recovery process, described below, a reduced number ofcandidate decodings are generated to recover data loss within a block.

As illustrated in DR Modular Shuffle 1410, a segment stores 880 DRvalues. Accordingly, the DR values are numbered 0-879 dependent on theblock from which a given DR value is derived. In a modular threeshuffling the FL-data contents of three segments are shuffled. A countof 0-2 is used to identify each DR value in the three segmentsidentified for a modular shuffling. Accordingly, DR's belonging toblocks numbered 0, 3, 6, 9 . . . belong to Count 0. Similarly, DR'sbelonging to blocks numbered 1, 4, 7, 10, . . . belong to Count 1 andDR's belonging to blocks numbered 2, 5, 8, 11 . . . belong to Count 2.Thus, for a given count the DR values associated with that count areshuffled across Segment 0, 2, and 4. Similarly, the DR valuesassociatedwith the same count are shuffled across Segments 1, 3, and 5.

In DR Modular Shuffle 1410, the DR values belonging to Count 0 are leftun-shuffled. The DR values belonging to Count 1 are shuffled. Inparticular, the Count 1 DR values in Segment A are moved to Segment B,the Count 1 DR values in Segment B are moved to Segment C, and the Count1 DR values in Segment C are moved to Segment A.

The DR values belonging to Count 2 are also shuffled. In particular, theCount 2 DR values in Segment A are moved to Segment C, the Count 2 DRvalues in Segment B are moved to Segment A, and the Count 2 DR values inSegment C are moved to Segment B.

MIN Modular Shuffle 1420 illustrates one embodiment of a modular threeblock attribute shuffling process for MIN values. A segment includes 880MIN values. In MIN Modular Shuffle 1420, the shuffling pattern used forCount 1 and Count 2 in DR Modular Shuffle 1410 are shifted to Count 0and Count 1. In particular, the shuffling pattern used for Count 1 in DRModular Shuffle 1410 is applied to Count 0. The shuffling pattern usedfor Count 2 in DR Modular Shuffle 1410 is applied to Count 1 and the MINvalues belonging to Count 2 are left un-shuffled.

Motion Flag Modular Shuffle 1430 illustrates one embodiment of a modularthree block attribute shuffling process for Motion Flag values. Asegment includes 880 Motion Flag values. In Motion Flag Modular Shuffle1430, the shuffling pattern used for Count 1 and Count 2 in DR ModularShuffle 1410 are shifted to Count 2 and Count 0 respectively. Inparticular, the shuffling pattern used for Count 2 in DR Modular Shuffle1410 is applied to Count 0. The shuffling pattern used for Count 1 in DRModular Shuffle 1410 is applied to Count 2 and the Motion Flag valuesbelonging to Count 1 are left un-shuffled.

FIG. 14a illustrates the modular shuffling result of Modular Shuffles1410, 1420, and 1430. Modular Shuffle Result 1416 shows each attributedestination of blocks belonging to Segment 0. In this example, Segment 0corresponds to Segment A of FIG. 14. This destination is definedaccordingto Modular Shuffles 1410, 1420, and 1430 of FIG. 14. FIG. 14aalso illustrates the distribution loss of block attributes after Segment0 is lost during transmission. In particular, Loss Pattern 1415 showsthe DR, Motion Flag, and MIN values loss across six segments after asubsequent deshuffling is applied to the received data that wasinitially shuffled using Modular Shuffles 1410, 1420, and 1430. Asillustrated in FIG. 14a, the block attribute loss is distributedperiodically across Segments 0, 2, and 4 while Segments 1, 3, and 5 haveno block attribute loss. Additionally, Spatial Loss Pattern 1417illustrates the deshuffled spatial distribution of damaged FL-data afterSegment 0 is lost during transmission. Spatial Loss Pattern 1417 showsthe DR, Motion Flag, and MIN value loss after a subsequent deshufflingis applied to the received data. In Spatial Loss Pattern 1417, a damagedblock is surrounded by undamaged blocks and damaged block attributes canbe recovered with surrounding undamaged blocks.

FIG. 14 and FIG. 14a illustrate a modular three shuffling pattern andthe distribution loss of block attributes after a segment is lost duringtransmission. In alternative embodiments, the count variables or thenumber of segments are varied to alternate the distribution of lostblock attributes. FIG. 14b illustrates Modular Shuffle Result 1421 andLoss Pattern 1420. Similarly, FIG. 14c illustrates Modular ShuffleResult 1426 and Loss Pattern 1425. Both Loss Pattern 1420 and LossPattern 1425 illustrate the distribution loss of block attributes acrosssix segments, as opposed to three segments as previously described.

It is contemplated that in alternate embodiments various combinations ofblock attributes will be distributed to perform the shuffling process.

Inter Segment VL-Data Shuffling

In the inter segment VL-data shuffling process, bits between apredetermined number of segments, for example, 6 segments, are arrangedto ensure a spatially separated and periodic VL-data loss during an upto ⅙ packet transmission loss. FIGS. 15 and 16 illustrate one embodimentof theinter segment VL-data shuffling process.

In the present embodiment, a transmission rate approaching 30 Mbps isdesired. Accordingly, the desired transmission rate results in 31,152bits available for the VL-data in each of the 60 buffers. The remainingspace is used by FL-data for the eighty eight blocks included in abuffer. FIG. 15 includes the VL-data buffer organization within a frameset for a transmission rate approaching 30 Mbps. As previouslydescribed, partial buffering is used to maximize the usage of availableVL-data space within each buffer, and the unused VL-data space is filledwith a post-amble.

FIG. 16 illustrates one embodiment of the shuffling process to ensure aspatially separated and periodic VL-data loss. The first row illustratesthe VL-data from the 60 buffers in FIG. 15 rearranged into aconcatenated stream of 1,869,120 bits. The second row illustrates thecollection of every sixth bit into a new stream of bits. Thus, when thedecoder subsequently reverses the process, a burst loss of up to ⅙ ofthe data transmitted is transformed into a periodic loss where at least5 undamaged bits separate every set of two damaged bits.

The third row illustrates grouping every 10 bits of Stream 2 into a newstream of bits, Stream 3. The boundary of a grouping is also defined bythe number of bits in a segment. Grouping of Stream 2 for every tenthbit ensures that a {fraction (1/60)} data loss results in fifty-nineundamaged bits between every set of two damaged bits. This provides fora spatially separated and periodic VL-data loss in the event that 88consecutive packets of data are lost.

The fourth row illustrates grouping every 11 bits of Stream 3 intoStream 4. The boundary of a grouping is also defined by the number ofbits in a segment. Grouping of Stream 3 for every eleventh bit ensuresthat {fraction (1/660)} data loss results in 659 undamaged bits betweento damaged bits, resulting in a spatially separated and periodic VL-dataloss during a transmission loss of 8 consecutive packets.

Each group of 31,152 bits within Stream 4 is consecutively re-stored inBuffers 0-59, with the first group of bits stored in Buffer 0 and thelast group of bits stored in Buffer 59.

It will be appreciated by one skilled in the art that the groupingrequirements of FIG. 16 are variable to ensure a spatially separated andperiodic VL-data loss tolerance up to a 1/n transmission loss.

Transmission

The previously described shuffling process creates buffers withintermixed FL-data and VL-data. For one embodiment, packets aregenerated from each buffer, according to packet structure 200, andtransmitted across Transmission media 135.

Data Recovery

As noted earlier, the innovative method for encoding the bitstream ofdata enables robust recovery of data that typically occurs due to lostpackets of data. The general overview of the decoding process has beenshown in FIG. 4.

Referring to FIG. 4, the data received in packets is processed throughthe multiple level deshuffling process, steps 425, 430, 435, and 440wherein different levels or portions of the bitstream received viapackets are deshuffled to retrieve data. ADRC decoding is then appliedto the data, step 445, in accordance with the teaching known in the art(e.g., Kondo, Fujimori, Nakaya, “Adaptive Dynamic Coding Scheme forFuture HDTV Digital VTR”, Fourth International Workshop on HDTV andBeyond, Sep. 4-6, 1991, Turin, Italy).

Intra frame set block deshuffling is then performed and block-to-imagemapping is subsequently executed, steps 450, 455. Steps 425,430,435,440,445,450, and 455 are inverse processes of the earlier process stepsperformed to encode the data and will not be discussed in detail herein.However, it should be noted that in one embodiment, deshuffling levelsrepresented by steps 425, 430 and 440 are data independent. For example,the deshuffling process performed is predetermined or specified by anaddress mapping or table lookup. Since deshuffling steps 425, 430 and440 are independent of data contents, data loss due to, for example,packet loss, does not prevent the deshuffling steps from beingperformed. Similarly, steps 450 and 455 are data independent. The intragroup VL-data deshuffling process, however, is dependent on the contentsof data. More particularly, the intra group VL-data deshuffling processis used to determine the quantization codes for the blocks of thegroups. Thus, at step 435, if packets are lost, the affected groupscannot be processed.

After execution of the deshuffling, decoding and mapping (steps 425,430, 435, 440, 445, 450 and 455), a recovery process is performed torecover the Qbit and Motion Flag values that were located in lostpackets. The Qbit value is lost typically due to DR loss (due to lostpackets). When the Qbit or Motion Flag value is unknown, the Q code bitsof a pixel cannot be determined from the data bitstream. If a Qbit orMotion Flag value is improperly determined then this error willpropagate as the starting point of subsequent blocks in that data in thebuffer will be incorrectly identified.

FIG. 17 describes the general process for recovering the Qbit and MotionFlag values. This particular embodiment describes the process usingmultiple blocks of data to recover the Qbit and Motion Flag values;however, it is contemplated that the particular number of blocks is notlimited by the discussion herein and could be one or more blocks.Referring to FIG. 17, based on the detection of an error in thebitstream, step 1705, candidate decodings based on specified parametersare generated for the three blocks examined. At step 1715, eachcandidate decoding is scored on the likelihood that it is an accuratedecoding and at step 1720, the candidate decoding with the best score isused, the decoding identifying the Qbit and Motion Flag values whichenable the subsequent decoding of pixels of the affected blocks.

Referring back to the decoding process of FIG. 4, once the best decodingis selected, any DR or MIN values that were lost due to lost packets arerecovered, step 465. A variety of recovery processes known to oneskilled in the art can be applied to recover DR and MIN, including leastsquares or the averaging of values of adjacent blocks. For one example,see, Kondo, Fujimori, Nakaya, “Adaptive Dynamic Coding Scheme for FutureHDTV Digital VTR”, Fourth International Workshop on HDTV and Beyond,Sep. 4-6, 1991 Turin, Italy. In the present embodiment, the innovativeimage-to-block mapping processes and data structures created therefromincrease the number of neighboring blocks, thus providing additionaldata and facilitating more accurate DR or MIN recovery. In particular,in one embodiment, DR and MIN are recovered as follows:${DR}^{\prime} = \frac{m \cdot {\sum\limits_{i}{\left( {y_{i} - {MIN}} \right) \cdot q_{i}}}}{\sum\limits_{i\quad}q_{i}^{2}}$

where DR′ corresponds to the recovered DR, q_(i) is the i-th value in anADRC block and q_(i)ε{0,1, . . . 2^(Q)−1}; m=2^(Q)−1 for Edge-matchingADRC and m=2^(Q) for Non-edge-matching ADRC; y_(i) is a decoded value ofan adjacent block pixel; and Q is the Qbit value; and${MIN}^{\prime} = \frac{\sum\limits_{i}\left( {y_{i} - {\frac{DR}{m} \cdot q_{i}}} \right)}{N}$

where MIN′ corresponds to the recovered MIN and N is the number of termsused in the summation (e.g., N=32 when i=0-31). In another embodiment,if DR and MIN of the same block are damaged at the same time, DR and MINare recovered according to the following equations:${DR}^{\prime} = \frac{m \cdot \left\lbrack {{N \cdot {\sum\limits_{i}{q_{i} \cdot y_{i}}}} - {\sum\limits_{i}{q_{i} \cdot {\sum\limits_{i}y_{i}}}}} \right\rbrack}{{N \cdot {\sum\limits_{i}q_{i}^{2}}} - \left\lbrack {\sum\limits_{i}q_{i}} \right\rbrack^{2}}$${MIN}^{\prime} = \frac{\sum\limits_{i}\left( {y_{i} - {\frac{{DR}^{\prime}}{m} \cdot q_{i}}} \right)}{N}$

At step 470, ADRC decoding is applied to those blocks not previouslydecoded prior to Qbit and Motion Flag recovery and a pixel recoveryprocess is executed, step 475, to recover any erroneous pixel data thatmay have occurred due to lost packets or random errors. In addition a3:1:0->4:2:2 back conversion is performed, step 480, to place the imagein the desired format for display.

FIG. 18 illustrates one particular embodiment of the Qbit and MotionFlag recovery process of the decoding process of the present invention.In this particular embodiment, the inputs to the process are adjacentblock information, and the block attributes and pixel data for the threeblocks to be processed. Error flags indicating the location of the lostdata are also input. The error flags can be generated in a variety ofways known to one skilled in the art and will not be discussed furtherherein except to say that the flags indicate which bits were transmittedby damaged or lost packets.

At step 1805, the candidate decodings are generated. The candidatedecodings can be generated a variety of ways. For example, although theprocessing burden would be quite significant, the candidate decodingscan include all possible decodings. Alternately, the candidate decodingscan be generated based on pre-specified parameters to narrow the numberof candidate decodings to be evaluated.

In the present embodiment, the candidate decodings are determined basedon the possible key values used to randomize a bitstream of the intragroup VL-data shuffling process earlier described. In addition, itshould be noted that candidate decodings are further limited by thelength of the bits remaining to be decoded and knowledge of how manyblocks remain. For example, as will be discussed, if processing the lastblock typically the decoding length of that block is known.

Continuing with the present example, FIG. 19 illustrates possible casesfor the present embodiment where the value x indicates an unknown value(which may be due to packet loss). This is further explained by example.m_(i) is defined as the Motion Flag of the i-th block, q_(i) is thenumber of the quantization bits of the i-th block, n_(i) is the numberof possible candidates of the i-th block and di is the value of a keyelement of the i-th block described previously in intra group VL-datashuffling. The i-th block is defined within each group. In this example,the number of blocks within each group is three. A key for the threeblock group is generated as, d₀+10·d₁+100·d₂. Assuming that in the firstblock the Motion Flag is unknown and the number of quantization bits is2, mo equals x and q₀ equals 2. Following the equation described aboveto generate the key element, d_(i)=5·m_(i)+q_(i), the set of possibledigits for d₀ consists of {2 and 7}. Thus, the number of possible values(n₀) is 2. Assuming the second block to have a Motion Flag value of 1and one quantization bit, and the value for d₁ is 5·1+1=6 and n₁=1. Thethird block has a Motion Flag value of 1 and an unknown number ofquantization bits. Thus, the digit d₂ includes a set consisting of {6,7, 8, 9} and n₂=4. Thus, the number of possible candidates of thisgroup, M, is 2·1·4=8, and the keys used to generate the candidatedecodings are the variations of 662, 667, 762, 767, 862, 867, 962, 967.This process is preferably used for each group which was affected bydata loss.

Referring back to FIG. 17, at step 1715, once the data has been decodedin accordance with the key data, the candidate decodings generated areevaluated or scored on the likelihood that it is a correct decoding ofthe data. At step 1720, the candidate decoding with the best score isselected to be used.

A variety of techniques can be used to score the candidate decodings.For example, the score may be derived from an analysis of how pixels ofblocks of a particular candidate decoding fit in with other pixels ofthe image. Preferably the score is derived based upon a criteriaindicative of error, such as a square error and correlation. Forexample, with respect to correlation, it is a fairly safe assumptionthat the adjacent pixels will be somewhat closely correlated. Thus, asignificant or a lack of correlation is indicative that the candidatedecoding is or is not the correct decoding.

As is shown in FIG. 18, four different criteria are analyzed to selectthe best candidate decoding. However, it is contemplated that one, two,three or more different criteria can be analyzed to select the bestcandidate decoding.

Referring to FIG. 18, the present embodiment utilizes four subscoringcriteria which are subsequently combined into a final score.Inparticular, in step 1815, the square error measure is generated, step1820, horizontal correlation is determined, step 1825, verticalcorrelation is determined, and at step 1830 temporal activity ismeasured (each an M-by-2·N matrix in accordance with M candidates, Nblocks and 2 frames/block of data). Although horizontal and verticalcorrelation is used, it should be recognized that a variety ofcorrelation measurements, including diagonal correlation, can beexamined. At steps 1835, 1840, 1845, 1850, a confidence measure isgenerated for each criterion to normalize the measurements generated,and at steps 1855, 1860, 1865 and 1870, a probability function for eachof the different criteria is generated. These probability functions arethen combined, for example, by multiplying the probability values togenerate a score, for example, the likelihood function shown in FIG. 18,step 1875. The score for the candidate decoding is subsequently comparedagainst all candidate decoding scores to determine the likely candidate.

It should be recognized that a variety of techniques can be used toevaluate the candidate decodings and generate the “scorings” for eachcandidate. For example, confidence measures are one way of normalizingthe criteria. Furthermore, a variety of confidence measures, besides theones described below, can be used. Similarly, multiplying theprobability values based on each criterion to generate a totallikelihood function is just one way of combining the variety of criteriaexamined.

The encoding processes facilitate the determination of the bestcandidate decoding because typically the candidate decodings which arenot the likely candidate, will have a relatively poor score, whiledecodings that are quite likely candidates will have a significantlybetter score. In particular, the Q code randomization process describedpreviously in the intra group VL- data shuffling process assists in thisregard.

FIGS. 20a 20 b, 20 c and 20 d provide illustrations of the differentmeasurements performed at steps 1815, 1820, 1825 and 1830 of FIG. 18 togenerate the scoring and total score for a particular candidatedecoding. FIG. 20a illustrates the square error to evaluate a candidatedecoded pixel x_(i) as compared to its decoded neighbors y_(i,j),wherein the suffix “i,j” is corresponding to the neighboring address of“i”. It is preferred that some of the largest terms are removed toremove any influences due to spikes, that is the terms that arise due tolegitimate edges in the image. Preferably, the three largest terms of(x_(i)−y_(i,j))² are discarded to remove spikes that may occurred. FIG.20b illustrates the temporal activity criteria. This is applicable onlywhen it is or is assumed to be a motion block. The temporal activitycriteria assumes that the better the candidate decoding, the smaller thedifferences between blocks. Thus the worse the candidate decoding, thelarger the differences between blocks. Spatial correlation assumes thatthe more likely candidate decodings will result in heavy correlations asreal images tend to change in a slow consistent way. The horizontalcorrelation process illustrated in FIG. 20c and vertical correlationprocess illustrated by FIG. 20d utilize that assumption.

The confidence measures, steps 1835, 1840, 1845, and 1850 of FIG. 18,provide a process for normalizing the criteria determined in theprevious steps (steps 1815, 1820, 1825 and 1830). In one embodiment, forexample, the confidence measure for the square error takes values fromthe interval [0,1], and confidence is equal to 0 if the errors are equaland equal to 1 if one error is 0. Other measures or methods to normalizeare also contemplated.

Similarly, the confidence measure for the spatial correlation is:

maximum(Y,0)−maximum(X,0)

where Y is the best correlation value and X is the correlation for thecurrent candidate decoding. The temporal activity confidence measure isdetermined according to the following equation:

conf=(a−b)/(a+b)

where a=max (X, M_TH) and b=max (Y,M_TH) where M_TH is the motionthreshold for the candidate block and Y is the best measurement, that isthe smallest temporal activity, and X equals the current candidatemeasurement of temporal activity.

At steps 1855, 1860, 1865 and 1870, FIG. 18, the probability function isgenerated for each of the different criteria. A variety of methods canbe used to generate the probability measure. For example, a score can beprescribed to a confidence measure. If the confidence measure is greaterthan a predetermined value, e.g., 0.8, the base score is decreased by10; if between 0.5 and 0.8, the base score decreased by 5. FIG. 21illustrates one embodiment in which a table used to generate theprobability function for the square error measurement criteria. Thetable includes empirically determined based data arbitrarily binnedcontaining confidence and square error measures and known candidatedecodings. More particularly, the table can be generated by usingundamaged data and assuming that the DR was corrupted or lost. Keys andconfidence measures for correct and incorrect decodings are thengenerated. The table reflects the probability ratio of correct toincorrect decodings. Using this table, for a particular squared errorvalue (row) and confidence value (column), the probability can bedetermined. For example, it can therefore be seen that for a variety ofsquare error measures at a confidence measure of zero, there isapproximately a 40% to 50% probability that the candidate is correct. Ifthe confidence is not 0, but small, the probability drops significantly.Similar probability tables are generated for the correlation andtemporal measurements based on corresponding empirically determinedcriteria measurements and confidence measurements.

The probabilities generated are considered data to generate “scores” inthe present embodiment and as noted earlier, other techniques to scorecandidate decodings may be used. At step 1875, the differentprobabilities are combined into a likelihood functionL_(i)=π_(j)·P_(i,j), where π_(j) is a multiplication function ofprobability functions P_(i,j), and P_(i,j), is the probability functionfor candidate i, block j. The candidate is therefore selected as the onethat maximizes the function L_(i).

Referring back to FIG. 18, it may be necessary to recover certain blockattributes that were transmitted in lost packets. Therefore, at step1810, DR and MIN values are recovered where necessary. A variety oftechniques, from default values, averaging, squared error functions tomore sophisticated techniques, including those discussed in Kondo,Fujimori and Nakaya, “Adaptive Dynamic Range Coding Scheme for FutureHDTV Digital VTR”, and Kondo, Fujimori, Nakaya and Uchida, “A NewConcealment Method for Digital VCRs”, IEEE Visual Signal Processing andCommunications, Sep. 20-22, 1993, Melbourne Australia, may be used. Therecovered values are utilized to generate the candidate decodings asdiscussed above.

Alternately, the DR and MIN values are determined during the Qbitdetermination process. This is illustrated in FIG. 22. In particular, asnoted above, in the present embodiment, the Motion Flag and number ofquantization bits are used in the encoding process and later used duringthe recovery process to narrow the number of possible candidatedecodings. As noted earlier, other information can also be used. Thusthe value of DR and/or value of MIN may also be used to encode the data.Alternately, a portion of bits of DR are used for encoding (e.g., thetwo least significant bits of DR). Although the DR data is encoded, thenumber of possible candidate decodings is increased significantly asvariables are added. Referring to FIG. 22, K·M candidate decodings aretherefore generated, where K is the number of candidate values for theunknown data, e.g. K=4 if two bits of the sum of DR₁, DR₂ and DR₃ isencoded (DR₁, DR₂ and DR₃ represent the DR values of the blocks of thegroup). The DR and MIN are therefore recovered using the auxiliaryinformation provided, e.g., the encoded two bits of the sum of DR₁, DR₂and DR₃. This improves the process of candidate selection at the cost ofadditional overhead to examine the larger number of candidate decodings.

It should be noted that generally, the more neighboring blocks that aredecoded, the better the Qbit and Motion Flag recovery process.Furthermore, in some embodiments the process is applied to eachsubsequent block of a buffer; if all or some of the FL-data isavailable, the number of candidate decodings can be reduced, possibly toone candidate decoding given all the FL-data for a block is available.However, it is desirable that the Qbit and Motion Flag recovery processbe avoided altogether as the process is a relatively time consuming one.Furthermore, it is desirable to use as much information as possible toperform Qbit and Motion Flag recovery. In one embodiment, blocks areprocessed from the beginning of a buffer until a block with lostQbit/Motion Flag information is reached. This is defined as forward Qbitand Motion Flag recovery. In another embodiment, the end of the bufferis referenced to determine the location of the end of the last block ofthe buffer and the data is recovered from the end of the buffer until ablock with lost Qbit/Motion Flag data is reached. This is defined asbackward Qbit and Motion Flag recovery.

As noted earlier, the blocks are variable in length, due the length ofthe VL-data; therefore there is a need to determine the number of bitsforming the VL-data of a block so that the position of subsequent blocksin the buffer can be accurately located. During the encoding process, apost-amble of a predetermined and preferably easily recognizable patternis placed in the buffer to fill the unused bit locations. During thedecoding process, the post-amble will be located between the block andthe end of the buffer. As the pattern is one that is easilyrecognizable, review of patterns of bits enables the system to locatethe beginning of the post-amble and therefore the end of the last blockin the buffer. This information can be used in two ways. If the lastblock contains damaged Qbit/Motion Flag data and the beginning of thelast block is known (e.g., the preceding blocks have been successfullydecoded), the difference between the end of the immediate precedingblock and the beginning of the post-amble corresponds to the length ofthe block. This information can be used to calculate the Qbit and/orMotion Flag of the block. The starting location of the post-amble canalso be used to perform Qbit and Motion Flag recovery starting at thelast block and proceeding towards the beginning of the buffer. Thus, theQbit and Motion Flag recovery process can be implementedbidirectionally.

FIG. 23 illustrates the use of a post-amble in the bidirectional Qbitand Motion Flag recovery process. Referring to FIG. 23, the buffer 2300includes FL-data 2303 for the N groups of blocks of VL-data. Each groupconsists of a plurality of blocks (e.g., 3 blocks). In the presentexample, the first two groups 2305, 2310 are decoded and the third group215 cannot immediately be decoded due to damaged DR/Motion Flag data. Atthis point, the Qbit/Motion Flag recovery process is required in orderto recover the damaged data. Rather than continue processing groups inthe forward direction, the process refers to the end of the buffer,determined by looking for the post-amble pattern 220. The beginning ofthe post-amble and therefore the end of the last group of blocks aredetermined. As the DR/Motion Flag data is indicative of the length ofthe VL-data, the beginning of the VL data of the last block, andtherefore the end of the immediate preceding block, is determined.Therefore, the blocks can be decoded , e.g., blocks 225, 230, 235 untila block 240 with damaged data is reached. The damaged 215, 240 andobstructed blocks 250 are then recovered, preferably using theQbit/Motion Flag recovery process described above.

It should be noted that the bidirectional process is not limited to asequence of forward and reverse processing; processing can occur ineither or both directions. Furthermore, in some embodiments, it may bedesirable to perform such processing in parallel to improve efficiency.Finally, it is contemplated that undamaged obstructed blocks may berecovered by directly accessing the Qbit/Motion Flag information withoutexecuting the Qbit/Motion Flag recovery process described above.

As noted earlier, a variety of scoring techniques may be used todetermine the best candidate decoding to select as the decoding. In analternate embodiment, the smoothness of the image using each candidatedecoding is evaluated. In one embodiment, the Laplacian measurement isperformed. The Laplacian measurement measures a second-order imagesurface property, e.g., surface curvature. For a linear image surface,i.e., smooth surface, the Laplacian measurement will result in a valuethat is approximately zero.

The process will be explained with reference to FIGS. 24a, 24 b, and 24c. FIG. 24a illustrates one embodiment of the Laplacian kernel. It iscontemplated that other embodiments may also be used. The kernel “L”represents a 3×3 region. To measure smoothness of the image region, 3×3subregions of the image (FIG. 24b) are convolved with the kernel and theconvolved values are averaged. The size of the region and subregion (andtherefore kernel size) can be varied according to application. Oneembodiment of the process is described with reference to FIG. 24c. Thisembodiment utilizes a kernel and subregion size of 3×3 and a region sizeof 8×8, the individual elements identified by indices i,j. At step 2460,the candidate decoded values x[i][j] are normalized. For example, thevalues can be normalized according to the following equation:${{{\overset{\sim}{x}\lbrack i\rbrack}\quad\lbrack j\rbrack} = \frac{{x\lbrack i\rbrack}\lbrack j\rbrack}{\sqrt{\sum\limits_{i,j}\left( {{{x\lbrack i\rbrack}\lbrack j\rbrack} - X_{mean}} \right)^{2}}}},{0 \leq i},{j < 8}$${where},{X_{mean} = \frac{\sum\limits_{i,j}{{x\lbrack i\rbrack}\lbrack j\rbrack}}{64}},{0 \leq i},{j < 8}$

At step 2465, the normalized values are used to compute a blockLaplacian value Lx indicative of smoothness according to the following:${{{l\lbrack i\rbrack}\quad\lbrack j\rbrack} = {\sum\limits_{m = {- 1}}^{1}{\sum\limits_{n = {- 1}}^{1}{{{L\lbrack m\rbrack}\lbrack n\rbrack} \cdot {{x^{\prime}\left\lbrack {i + m} \right\rbrack}\quad\left\lbrack {j + n} \right\rbrack}}}}},{0 \leq i},{j < 8}$$L_{x} = \frac{\sum\limits_{i,j}{{{l\lbrack i\rbrack}\quad\lbrack j\rbrack}}}{64}$

The closer the block Laplacian value is to zero, the smoother the imageportion. Thus a score can be measured based upon the block Laplacianvalue, and the decoding with the least Laplacian value is the correctone.

The Laplacian evaluation can also be achieved using candidate encodedvalues q[i][j]. The basic process is the same as the candidate decodedvalue case of FIG. 24c. This embodiment utilizes a kernel and subregionsize of 3×3 and a region size 8×8, the individual elements identifies bythe indices i,j. At step 2460, the candidate encoded values q[i][j] arenormalized. For example, the values can be normalized according to thefollowing equation:${{{q^{\prime}\lbrack i\rbrack}\quad\lbrack j\rbrack} = \frac{{q\lbrack i\rbrack}\lbrack j\rbrack}{\sqrt{\sum\limits_{i,j}\left( {{{q\lbrack i\rbrack}\lbrack j\rbrack} - Q_{mean}} \right)^{2}}}},{0 \leq i},{j < 8}$${where},{Q_{mean} = \frac{\sum\limits_{i,j}{{q\lbrack i\rbrack}\lbrack j\rbrack}}{64}}$

At step 2465, the normalized values are used to compute the blockLaplacian value L_(q) indicative of smoothness according to thefollowing equation:${{{l\lbrack i\rbrack}\quad\lbrack j\rbrack} = {\sum\limits_{m = {- 1}}^{1}{\sum\limits_{n = {- 1}}^{1}{{{L\lbrack m\rbrack}\lbrack n\rbrack} \cdot {{q^{\prime}\left\lbrack {i + m} \right\rbrack}\quad\left\lbrack {j + n} \right\rbrack}}}}},{0 \leq i},{j < 7}$$L_{q} = \frac{\sum\limits_{i,j}{{{l\lbrack i\rbrack}\quad\lbrack j\rbrack}}}{36}$

The closer the block Laplacian value is to zero, the smoother the imageportion. Thus a score can be measured based upon the block Laplacianvalue and the candidate with the smallest Laplacian value is the correctone.

Other variations are also contemplated. In alternative embodiments,higher order image surface properties can be used as a smoothnessmeasure. In those cases, higher order kernels would be used. Forexample, a fourth order block Laplacian measurement may be performedusing a fourth order kernel. Such a fourth order kernel can be realizedusing two second order Laplacian computations in cascade.

It is further contemplated that the evaluation process is dependent uponwhether the image has an activity or motion larger than a predeterminedlevel. If the image portion is evaluated to have larger motion than apredetermined level, then it is preferable to perform the measurementson a field basis as opposed to on a frame basis. This is explained withreference to FIG. 25. FIG. 25 explains the process using smoothnessmeasures; however, it is contemplated that this process can beimplemented using a variety of types of measures.

Frame 2505 of an image region is composed of field 0 and field 1. Ifmotion is not detected, step 2510, the smoothness measurement iscomputed by computing the block Laplacian value for the block withineach frame, step 2515. If larger motion than a predetermined level isdetected, block Laplacian measurements are performed on each field,steps 2520, 2525 and the two measurements are combined, step 2530, e.g.,averaged to generate the smoothness measurement.

Motion can be detected/measured a variety of ways. In one embodiment,the extent of change between fields is evaluated and motion is detectedif it exceeds a predetermined threshold.

Motion detection and the use of frame information and field informationto generate recovered values (typically to replace lost or damagedvalues) can be applied to any portion of the process that requires arecovered value to be generated. For example, motion detection and theselective use of frame information and field information to generaterecovered values can be applied to DR/MIN recovery, pixel recovery aswell as Qbit and Motion Flag recovery processes. Thus, based on thelevel of motion detected, the recovery process will utilize existinginformation on a field basis or frame basis. Furthermore, this processcan be combined with the application of weighting values that areselected based upon levels of correlation in particular directions(e.g., horizontal or vertical).

In another embodiment of the Qbit and Motion Flag recovery process,candidate decodings are evaluated based upon intra block and inter blockmeasurements. In the following discussion, the term “block” refers to aportion of a frame or field. The intra block measurement evaluates thecandidate decoded image portion, e.g., the smoothness of the imageportion. The inter block measurement measures how well the candidatedecoding fits with the neighboring image portions. FIGS. 26a and 26 billustrate the combined inter block and intra block evaluation. Inparticular, FIG. 26a shows an acceptable candidate decoding as both theinter block and intra block measurements are good, whereas in FIG. 26bthe inter block measurement is poor, even though the intra blockmeasurement is quite good.

Examples of intra block measurements include the smoothness measurementdescribed above. Examples of inter block measurements include the squareerror measurements described earlier. An alternative inter blockmeasurement is the ratio of compatible boundary pixels and the totalnumber of boundary pixels at the candidate ADRC block.

An example of an inter block and intra block evaluation of an 8×8 blockthat is ADRC encoded will be explained with respect to FIGS. 26c, 26 dand 26 e. FIG. 26d illustrates an image portion (block) of data of aencoded values 2650 consisting of q values from which candidate decodedvalues x are generated and neighboring decoded data 2655 consisting of yvalues. As set forth in the flow chart of FIG. 26c, at step 2605, theintra block measure is computed to generate a measure, e.g., blockLaplacian L_(x). At step 2610, the inter block measure S_(x) is computedto generate a measure of compatibility between adjacent blocks. At step2615, the combined measure M_(x) is generated. The combined measureprovides the information used to select a candidate decoding.

In the present embodiment, S_(x) is computed as the number ofneighboring data that lies in a valid range for each boundary pixel ofcandidate decoding (see FIG. 26e). FIG. 26e is a chart illustrating avalid range for one embodiment which shows a valid range of eachobserved quantized value q_(i). Thus L_(Q)≦DR<U_(Q), where L_(Q), U_(Q)respectively represent the lower and upper bounds of DR corresponding tothe number of quantization bits=Q. Preferably S_(x) is normalizedaccording to the following: S_(x)=S_(x)/number of boundary pixels.

In the present embodiment the combined measure M_(x) is computedaccording to the following equation: M_(x)=S_(x)+(1−L_(x)).Alternatively, the combined measure may be weighted such that thefollowing equation would be used: M_(X)=w·S_(x)+(1−w)·(1−L_(x)), where wis the weighting value, typically an empirically determined weightingvalue.

Other embodiments for determining DR and MIN values that have beenlost/damaged are also contemplated. For example, the earlier describedequations can be modified to recover DR and MIN values with higheraccuracy. In an alternate embodiment, a median technique is applied. Inone embodiment of the median technique, the value of MIN is recovered asthe median of all MINI values computed as:

MIN_(i) =y _(i) −q _(i) ·s

where q_(i) represents the encoded pixel value and y_(i) represents thedecoded pixel neighboring q_(i). For edge-matching ADRC, s=DR/(2^(Q)−1).For non-edge-matching ADRC, s=DR/2^(Q), where Q represents the number ofquantization bits per pixel (Qbit value).

The values used may be temporally proximate or spatially proximate. Thevalues of y_(i) may be the decoded value of the neighboring pixel in anadjacent frame/field or the same field. The values of y_(i) may be thedecoded value of the pixel from the same location as q_(i) in anadjacent frame/field or the same field.

In addition, any DR and/or MIN recovery technique may be combined with aclipping process to improve recovery accuracy and prevent data overflowduring the recovery process. The clipping process restricts therecovered data to a predetermined range of values; thus those valuesoutside the range are clipped to the closest range bound. In oneembodiment, the clipping process restricts values in the range [L_(Q),U_(Q)], where L_(Q), U_(Q) respectively represent the lower and upperbounds of the range of pixel values represented by the number ofquantization bits=Q. quantization bits, and further restricts values to:MIN+DR≦Num, where Num represents the maximum pixel value; in the presentembodiment, Num is 255. In the present embodiment, where applicable,U_(Q)+1=L_(Q+1).

Combining the criteria into a single equation results for an unboundedrecovered value (val′) for the DR, the final clipped recovered value(val) is obtained from the following equation:

val=max(min(val, min(U _(Q),255−MIN)),L _(Q))

where min and max respectively represent minimum and maximumfunctions.

In an alternate embodiment, the boundary pixels y_(i) used to generatean recovered DR and/or MIN can be filtered to only use those that appearto correlate best, thereby better recovering DR and MIN. Those boundarypixels not meeting the criteria are not used. In one embodiment, aboundary pixel y_(i) is considered valid for DR calculations if thereexists a value of DR such that L_(Q)≦DR<U_(Q) and an original pixely_(i) would have been encoded as q_(i). Thus, a pixel is valid if thefollowing equations are satisfied:$\frac{\left( {y_{i} - {MIN}} \right)m}{\max \left( {{q_{i} - 0.5},0} \right)} \geq L_{Q}$$\frac{\left( {y_{i} - {MIN}} \right)m}{\max \left( {{q_{i} - 0.5},m} \right)} < U_{Q}$

where m represents the maximum quantization level=2^(Q)−1. A DRrecovered value (val′) can then be computed according to the followingequation:${val}^{\prime} = \frac{m \cdot {\sum\limits_{i}{\left( {y_{i} - {MIN}} \right)q_{i}}}}{\sum\limits_{i}q_{i}^{2}}$

The value can then be clipped into the valid range. Thus this processforces the DR recovered value into the interior of the valid region asdefined by the threshold table, reducing the accuracy for points whosetrue DR lies near the threshold table boundary.

It has been noted that due to quantization noise, the DR of stationaryADRC blocks varies slightly from frame to frame. If this variancecrosses an ADRC encoding boundary, and if the DR is recovered on severalconsecutive frames, then the DR recovered value with valid pixelselection tends to overshoot at each crossing, resulting in a noticeableblinking effect in the display. In an attempt to reduce the occurrenceof this effect, in one embodiment, the valid pixel selection process ismodified to relax the upper and lower bounds, allowing border pixelsthat encroach into the neighboring valid region. By including pointsjust outside the boundary, it is more likely that the recovered valuewill take on a value near that of the upper or lower bound. The relaxedbounds L′_(Q) and U′_(Q) are computed by means of a relaxation constantr. In one embodiment, r is set to a value of 0.5. Other values can beused:

L′ _(Q) =rL _(Q−1)+(1−r)L _(Q)

U′ _(Q)=(1−r)U _(Q) +rU _(Q+1)

The discussion above sets forth a number of ways to recover DR and MINwhen the values have been damaged or lost. Further enhancements can berealized by examining the correlation between data temporally and/orspatially, and weighting corresponding calculated recovered valuesaccordingly. More particularly, if there is a large correlation in aparticular direction or across time, e.g., horizontal correlation, thereis a strong likelihood that the image features continue smoothly in thatdirection that has a large correlation and therefore an recovered valueusing highly correlated data typically generates a better estimate. Totake advantage of this, boundary data is broken down into correspondingdirections (e.g., vertical, horizontal, field-to-field) and weightedaccording to the correlation measurement to generate a final recoveredvalue.

One embodiment of the process is described with reference to FIG. 27a.At step 2710, a recovered value of the DR or MIN value to be recoveredis generated in one direction and at step 2715, a recovered value isgeneratedin another direction. For example, if the process is spatiallyadaptive, then boundary pixels along horizontal borders are used togenerate a first recovered value, “hest”, and boundary pixels alongvertical borders are used to generated a second recovered value, “vest”.Alternately, if the process is temporally adaptive, then boundary pixelsbetween adjacent fields are used to generate a first recovered value andboundary pixels between adjacent frames are used to generate a secondrecovered value.

At step 2720, the recovered values are weighted according to correlationcalculations indicative of the level of correlation in each direction.The weighted first and second recovered values are combined to generatea combined recovered value, step 2725. It should be noted that theprocess isnot limited to generated weighted recovered values in only twodirections; it is readily apparent that the number of recovered valuesthat are weighted and combined can be varied according to application. Avariety of known techniques can be used to generate a correlation valueindicative of the level of correlation in a particular direction.Furthermore, a variety of criteria can be used to select the weightingfactor in view of the levels of correlation. Typically, if onecorrelation is much larger than the other, the combined recovered valueshould be based primarily on the corresponding recovered value. In oneembodiment, the combined recovered value is computed as follows:${val}^{\prime} = \begin{Bmatrix}{{\alpha \quad {hest}} + {\left( {1 - \alpha} \right){{vest}:}}} & {{hc} \geq {vc}} \\{{\left( {1 - \alpha} \right){hest}} + {\alpha \quad {{vest}:}}} & {{hc} < {vc}}\end{Bmatrix}$

where hc represents the horizontal correlation, vc represents thevertical correlation, hest represents a DR recovered value based only onleft and right boundary information, and vest represents a DR recoveredvalue based only on top and bottom boundary information, and arepresents the weighting value. The weighting value can be determined avariety of ways. FIG. 27b illustrates one embodiment for determiningweighting values as a function of the difference between the horizontalcorrelation and vertical correlation. More particularly, a was chosen tobe: ${\alpha \left( {{{hc} - {vc}}} \right)} = \begin{Bmatrix}{0.5 + {0.25 \cdot {^{{- 8}{({0.35 - {{{hc} - {vc}}}})}}:}}} & {{{{hc} - {vc}}} < 0.35} \\{1 - {0.25 \cdot {^{{- 8}{({{{{hc} - {vc}}} - 0.35})}}:}}} & {{{{hc} - {vc}}} \geq 0.35}\end{Bmatrix}$

As noted above, the adaptive correlation process is applicable to bothDR and MIN recovery. It is preferred, however, that the MIN recovery isclipped to insure that MIN+DR≦255, therefore the functionval=max(min(val′, 255−MIN), 0) can be used. Furthermore, as noted above,the temporal correlation can be evaluated and used to weight recoveredvalues. In addition, a combination of temporal and spatial correlationcan be performed. For example, one recovered value is generated betweenfields as a temporal recovered value. Another recovered value isgenerated within one field as a spatial recovered value. The finalrecovered value is computed as the combination value with a combinationof temporal and spatial correlation. The correlation combination can bereplaced with a motion quantity. Other variations are also contemplated.The method can also be applied to audio data.

In an alternate embodiment, a low complexity modification to the leastsquares technique is used. Using this embodiment, the blinkingexperienced due to recovered DR values is reduced. For purposes of thefollowing discussion, QV represents a list of encoded values from theimage section or ADRC block whose DR is being recovered having a set ofpoints q_(i) and Y is a list of decoded values taken from the verticalor horizontal neighbors of the points in QV, where y_(i) represents avertical or horizontal neighbor of q_(i). As each point q_(i) may haveup to four decoded neighbors, one pixel or point may give rise to asmany as four (q_(i), y_(i)) pairings. The unconstrained least squaresestimate of DR (DR_(uls)) is thus:$({DR})_{uls} = \frac{2^{Q} \cdot {\sum\limits_{i}{\left( {y_{i} - {MIN}} \right) \cdot \left( {0.5 + q_{i}} \right)}}}{\sum\limits_{i}\left( {0.5 + q_{i}} \right)^{2}}$

where Q is the number of quantization bits, MIN is the minimum valuetransmitted as a block attribute. The above equation assumesnon-edge-matching ADRC; for edge-matching ADRC, 2^(Q) is replaced with2^(Q)−1 and (0.5 +q_(i)) is replaced with q_(i).

The unconstrained least squares estimate is preferably clipped to assureconsistency with the threshold table and the equation MIN+DR≦255 whichis enforced during encoding (Typically, for non-edge-matching ADRC,permissible DR values are in the range of 1-256). Thus, the leastsquares estimate is clipped (DR_(lsc)) by:

(DR)_(lsc)=max(min(UB,DR _(uls)),LB)

where UB represents the upper bound and LB represents the lower boundand min and max respectively represent minimum and maximum functions.

In an alternate embodiment, the estimation can be enhanced by selectingthe pixels that are more suitable for DR estimation to calculate theestimate of DR. For example, flat regions in an image provide pixelswhich are more suitable for DR estimation than those regions in whichhigh activity occurs. In particular, a sharp edge in the edge maydecrease the accuracy of the estimate. The following embodiment providesa computationally light method for selecting the pixels to use tocalculate an estimate of DR.

In one embodiment, the least squares estimate (DR_(lse)), e.g., DR_(uls)or DR_(lsc). is computed. Using this estimate, the list of encodedvalues QV is transformed into candidate decoded values X, where x_(i)are members of X derived from q_(i). The x_(i) value is a recovereddecoded value formed using the first estimate of DR. The x_(i) value isdefined according to the following equation:${{Edge}\text{-}{matching}\quad {ADRC}\text{:}\quad x_{i}} = {{MIN} + \left( {0.5 + \frac{q_{i} \cdot {DR}_{lse}}{2^{Q} - 1}} \right)}$${{Non}\text{-}{edge}\text{-}{matching}\quad {ADRC}\text{:}\quad x_{i}} = {{MIN}\quad + \left( \frac{\left( {q_{i} + 0.5} \right) \cdot {DR}_{lse}}{2^{Q}} \right)}$

Assuming DR_(lse) is a reasonable estimate of the true DR, then anywherethat x_(i) is relatively close to y_(i), may be judged to be a lowactivity area and thus a desirable matching. New X and Y lists may thenbe formed by considering only the matches where x_(i) and y_(i) areclose and the least squares estimate recomputed to generate an updatedestimate.

The criteria for determining what is considered “close” can bedetermined a number of ways. In one embodiment, an ADRC encoding of theerror function is used. This approach is desirable as it iscomputationally inexpensive. For the process, a list E, consisting ofthe points e_(i)=|y_(i)−x_(i)| is defined. Defining emin and emax asrespectively the smallest and largest values from the list, theneDR=emax−emin. An encoded error value can then defined as:

g _(i)=(e _(i)−emin)nl/eDR

where nl represents the number of quantization levels for requantizinge_(i) in a similar manner to the ADRC process described above.

Thus, new lists X and Y are generated by selecting only those matcheswhere g_(i) is less than some threshold. If the new lists aresufficiently long, these lists may be used to generate a refined leastsquares estimate DR_(rls). The threshold for g_(i) and the number ofmatches needed before refining the least squares estimation ispreferably empirically determined. For example, in one embodiment for anprocess involving 8×8×2 horizontally subsampled blocks and nl is 10,only matches corresponding to g_(i)=0 are used, and the estimate isrefined only when the new lists contain at least 30 matches.

In an alternate embodiment, DR estimation can be improved by clippingpotential DR values and recomputing a DR estimate. In particular, in oneembodiment, a list D is composed of member d_(i) which contains the DRvalue that would cause x_(i) to equal y_(i). More precisely:

d _(i)=2^(Q)(y _(i)−MIN)/(0.5 +q _(i))

Improvement is seen by clipping each d_(i). That is,

d _(i)′=max(min(UB,d _(i)),LB)

where DR_(cls) is then computed to be the average of d_(i)′. The clippedmethod (DR_(cls)) may be combined with other DR estimates, e.g.,DR_(lse) in a weighted average to produce a final DR value. For example,the weighted average DR_(est) is determined according to the following:

DR _(est) =w ₁(DR _(cls))+w ₂(DR _(lse)).

The weights w₁ and w₂ are preferably empirically determined by examiningresultant estimations and images generated therefrom from particularweightings. In one embodiment w₁=0.22513 and w₂=0.80739.

The invention has been described in conjunction with the preferredembodiment. It is evident that numerous alternatives, modifications,variations and uses will be apparent to those skilled in the art inlight of the foregoing description.

What is claimed is:
 1. A method for recovering damaged data in abitstream of encoded data comprising the steps of: generating aplurality of candidate decodings based on specified parameters;calculating at least one an evaluation measurement for each of saidplurality of candidate decodings, according to the specified parameters,each of said evaluation measurements being determined based upon how acorresponding candidate decoding fits in with other decoded data of thebitstream of encoded data and according to the specified parameters;selecting a candidate decoding of the pulurality of candidate decodings;determining at least one specific parameter for the candidate decoding;and decoding the bitstream based upon the at least one specifiedparameter.
 2. The method as set forth in claim 1, wherein saidcalculation of said evaluation measurement further comprises the stepsof: measuring motion of said plurality of candidate decodings;determining a plurality of motion groups by evaluating said motion ofsaid plurality of candidate decodings; and calculating an evaluationmeasurement of said plurality of candidate decodings according to saidplurality of motion groups.
 3. The method as set forth in claim 2,wherein said encoded data is generated from a video signal, said videosignal comprising a plurality of video frames, said determination ofsaid plurality of motion groups further comprising the steps of:measuring a spatial and temporal correlation of said plurality ofcandidate decodings of said video frames; and determining said pluralityof motion groups by comparing a threshold to said spatial and temporalcorrelation.
 4. The method as set forth in claim 2, wherein said encodeddata is generated from a video signal, said video signal comprising aplurality of interlacing fields, said determination of said plurality ofmotion groups further comprising the steps of: measuring a spatial andtemporal correlation of said plurality of candidate decodings of saidinterlacing fields; and determining said plurality of motion groups bycomparing a threshold to said spatial and temporal correlation.
 5. Themethod as set forth in claim 2, wherein said encoded data is generatedfrom a video signal, said video signal comprising a plurality of videoframes, said determination of said plurality of motion groups furthercomprising the steps of: measuring a sum of changed samples of saidplurality of candidate decoding of said video frames; and determiningsaid plurality of motion groups by comparing a threshold to said sum ofchanged samples.
 6. The method as set forth in claim 2, wherein saidencoded data is generated from a video signal, said video signalcomprising a plurality of interlacing fields, said determination of saidplurality of motion groups further comprising the steps of: measuring asum of changed samples of said plurality of candidate decoding of saidinterlacing fields; and determining said plurality of motion groups bycomparing a threshold to said sum of changed samples.
 7. The method asset forth in claim 2, wherein said encoded data is generated from avideo signal, said video signal comprising a plurality of video frames,said determination of said plurality of motion groups further comprisingthe steps of: measuring a maximum absolute value of changed samples ofsaid plurality of candidate decodings of said video frames; anddetermining said plurality of motion groups by comparing a threshold tosaid maximum absolute value of changed samples.
 8. The method as setforth in claim 2, wherein said encoded data is generated from a videosignal, said video signal comprising a plurality of interlacing fields,said determination of said plurality of motion groups further comprisingthe steps of: measuring a maximum absolute value of changed samples ofsaid plurality of candidate decodings of said interlacing fields; anddetermining said plurality of motion groups by comparing a threshold tosaid maximum absolute value of changed samples.
 9. The method as setforth in claim 1, wherein calculating said evaluation measurementfurther comprises the steps of: normalizing candidate decoded values ofsaid plurality of candidate decodings; and calculating said evaluationmeasurement of said plurality of candidatedecodings.
 10. The method asset forth in claim 1, wherein said evaluation measurement is asmoothness measurement.
 11. The method as set forth in claim 10, whereincalculating said evaluation measurement further comprises the steps ofnormalizing {tilde over (x)}[i][j], where {tilde over (x)}[i][j] is thecandidate decoded value at location [i,j] in an area of the candidatedecoded image; and computing smoothness using the normalized {tilde over(x)}[i][j], Laplacian kernel L[m][n], and a block Laplacian value L_(x),determined according to the following:${{{l\lbrack i\rbrack}\quad\lbrack j\rbrack} = {\sum\limits_{m = {- 1}}^{1}{\sum\limits_{n = {- 1}}^{1}{{{L\lbrack m\rbrack}\lbrack n\rbrack}{{\overset{\sim}{x}\left\lbrack {i + m} \right\rbrack}\quad\left\lbrack {j + n} \right\rbrack}}}}},{0 \leq i},{j < K}$${L_{x} = \frac{\sum\limits_{i,j}{{{l\lbrack i\rbrack}\quad\lbrack j\rbrack}}}{K \cdot K}},{0 \leq i},{j < {K.}}$


12. The method as set forth in claim 11, where the step of normalizingis computed according to the following equation:${{{\overset{\sim}{x}\lbrack i\rbrack}\quad\lbrack j\rbrack} = \frac{{x\lbrack i\rbrack}\lbrack j\rbrack}{\sqrt{\sum\limits_{i,j}\left( {{{x\lbrack i\rbrack}\lbrack j\rbrack} - X_{mean}} \right)^{2}}}},{0 \leq i},{j < K}$${where},{X_{mean} = \frac{\sum\limits_{i,j}{{x\lbrack i\rbrack}\lbrack j\rbrack}}{K \cdot K}},{0 \leq i},{j < {K.}}$


13. The method as set forth in claim 12, wherein K is equal to
 8. 14.The method as set forth in claim 10, wherein the smoothness measurecomprises a second order image surface property.
 15. The method as setforth in claim 10, wherein the smoothness measure comprises an N orderimage surface property, where N>1.
 16. The method as set forth in claim10, wherein an image portion comprises a portion of a frame of data,said frame portion comprising portions of first and second interlacingfields of data, the step of calculating said evaluation measurementfurther comprising the steps of: determining if the image portionincludes motion; if the image portion includes motion; computing asmoothness measurement for a first field portion of the image portionand a second field portion of the image portion; and averaging thesmoothness measurements together to generate the smoothness measure; andif the image portion does not include motion, generating the smoothnessmeasure for the frame portion of the image portion.
 17. The method asset forth in claim 1, wherein said evaluation measurement is a Laplacianmeasurement.
 18. The method as set forth in claim 1, wherein saidevaluation measurement is a square error measurement.
 19. The method asset forth in claim 1, wherein said evaluation measurement is a spatialcorrelation measurement.
 20. The method as set forth in claim 1, whereinsaid evaluation measurement is a horizontal correlation measurement. 21.The method as set forth in claim 1, wherein said evaluation measurementis a vertical correlation measurement.
 22. The method as set forth inclaim 1, wherein said evaluation measurement is a diagonal correlationmeasurement.
 23. The method as set forth in claim 1, wherein saidevaluation measurement is a temporal activity measurement.
 24. Themethod of claim 1, wherein the specified parameters are selected fromthe group consisting of motion flag values and quantization bit values.25. A digital processing system comprising a processor configured torecover damaged data in a bitstream of encoded data, said processorconfigured to: generate a plurality of candidate decodings based onspecified parameters; calculate at least one an evaluation measurementfor each of said plurality of candidate decodings, according to thespecified parameters, each of said evaluation measurements beingdetermined based upon how a corresponding candidate decoding fits inwith other decoded data of the bitstream of encoded data and accordingto the specified parameters; selecting a candidate decoding of thepulurality of candidate decodings; determine at least one specificparameter for the candidate decoding; and decode the bitstream basedupon the at least one specified parameter.
 26. The digital processingsystem as set forth in claim 25, said processor further configured tocalculate the evaluation measurement for each of said plurality ofcandidate decodings by measuring motion of said plurality of candidatedecodings, determining a plurality of motion groups by evaluating saidmotion of said plurality of candidate decodings, and calculating theevaluation measurement of said plurality of candidate decodingsaccording to said plurality of motion groups.
 27. The digital processingsystem as set forth in claim 25, wherein said encoded data is generatedfrom a video signal, said video signal comprising a plurality of videoframes, said processor further configured to determine said plurality ofmotion groups by measuring a spatial and temporal correlation of saidplurality of candidate decodings of said video frames, and determiningsaid plurality of motion groups by comparing a threshold to said spatialand temporal correlation.
 28. The digital processing system as set forthin claim 25, wherein said encoded data is generated from a video signal,said video signal comprising a plurality of interlacing fields, saidprocessor further configured to determine said plurality of motiongroups by measuring a spatial and temporal correlation of said pluralityof candidate decodings of said interlacing fields, and determining saidplurality of motion groups by comparing a threshold to said spatial andtemporal correlation.
 29. The digital processing system as set forth inclaim 25, wherein said encoded data is generated from a video signal,said video signal comprising a plurality of video frames, said processorfurther configured to determine said plurality of motion groups bymeasuring a sum of changed samples of said plurality of candidatedecodings of said video frames, and determining said plurality of motiongroups by comparing a threshold to said sum of changed samples.
 30. Thedigital processing system as set forth in claim 25, wherein said encodeddata is generated from a video signal, said video signal comprising aplurality of interlacing fields, said processor further configured todetermine said plurality of motion groups by measuring a sum of changedsamples of said plurality of candidate decodings of said interlacingfields, and determining said plurality of motion groups by comparing athreshold to said sum of changed samples.
 31. The digital processingsystem as set forth in claim 25, wherein said encoded data is generatedfrom a video signal, said video signal comprising a plurality of videoframes, said processor further configured to determine said plurality ofmotion groups by measuring a maximum absolute value of changed samplesof said plurality of candidate decodings of said video frames, anddetermining said plurality of motion groups by comparing a threshold tosaid maximum absolute value of changed samples.
 32. The digitalprocessing system as set forth in claim 25, wherein said encoded data isgenerated from a video signal, said video signal comprising a pluralityof interlacing fields, said processor further configured to determinesaid plurality of motion groups by measuring a maximum absolute value ofchanged samples of said plurality of candidate decodings of saidinterlacing fields, and determining said plurality of motion groups bycomparing a threshold to said maximum absolute value of changed samples.33. The digital processing system as set forth in claim 25, saidprocessor further configured to calculate the evaluation measurement ofsaid plurality of candidate decodings by normalizing candidate decodedvalues of said plurality of candidate decodings, and calculating saidevaluation measurement of said plurality of candidate decodings.
 34. Thedigital processing system as set forth in claim 25, said processorfurther configured to calculate said evaluation measurement of saidplurality of candidate decodings, wherein said evaluation measurement isa smoothness measurement.
 35. The digital processing system as set forthin claim 34, wherein the processor is configured to calculate saidevaluation measurement by: normalizing {tilde over (x)}[i][j], where{tilde over (x)}[i][j] is the candidate decoded value at location [i,j]in an area of the candidate decoded image; and computing smoothnessusing the {tilde over (x)}[i][j], Laplacian kernel L[m][n], and a blockLaplacian value L_(x), determined according to the following:${{{l\lbrack i\rbrack}\quad\lbrack j\rbrack} = {\sum\limits_{m = {- 1}}^{1}{\sum\limits_{n = {- 1}}^{1}{{{L\lbrack m\rbrack}\lbrack n\rbrack}{{\overset{\sim}{x}\left\lbrack {i + m} \right\rbrack}\quad\left\lbrack {j + n} \right\rbrack}}}}},{0 \leq i},{j < K}$${L_{x} = \frac{\sum\limits_{i,j}{{{l\lbrack i\rbrack}\quad\lbrack j\rbrack}}}{K \cdot K}},{0 \leq i},{j < {K.}}$


36. The digital processing system as set forth in claim 35, where theprocessor is configured to normalize according to the followingequation:${{{\overset{\sim}{x}\lbrack i\rbrack}\quad\lbrack j\rbrack} = \frac{{x\lbrack i\rbrack}\lbrack j\rbrack}{\sqrt{\sum\limits_{i,j}\left( {{{x\lbrack i\rbrack}\lbrack j\rbrack} - X_{mean}} \right)^{2}}}},{0 \leq i},{j < K}$${where},{X_{mean} = \frac{\sum\limits_{i,j}{{x\lbrack i\rbrack}\lbrack j\rbrack}}{K \cdot K}},{0 \leq i},{j < {K.}}$


37. The digital processing system as set forth in claim 36, wherein K isequal to
 8. 38. The digital processing system as set forth in claim 37,wherein the smoothness measure comprises a second order image surfaceproperty.
 39. The digital processing system as set forth in claim 34,wherein the smoothness measure comprises an N order image surfaceproperty, where N>1.
 40. The digital processing system as set forth inclaim 34, wherein an image portion comprises a portion of a frame ofdata, said frame portion comprising portions of a first and secondinterlacing field of data, the processor configured to measure thesmoothness by: determining if the image portion includes motion; if theimage portion includes motion; computing a smoothness measurement for afirst field portion of the image portion and a second field portion ofthe image portion; and averaging the smoothness measurements together togenerate the smoothness measure; and if the image portion does notinclude motion, generating the smoothness measure for the frame portionof the image portion.
 41. The digital processing system as set forth inclaim 25, said processor further configured to calculate said evaluationmeasurement of said plurality of candidate decodings, wherein saidevaluation measurement is a Laplacian measurement.
 42. The digitalprocessing system as set forth in claim 25, said processor furtherconfigured to calculate said evaluation measurement of said plurality ofcandidate decodings, wherein said evaluation measurement is a squareerror measurement.
 43. The digital processing system as set forth inclaim 25, said processor further configured to calculate said evaluationmeasurement of said plurality of candidate decodings, wherein saidevaluation measurement is a spatial correlation measurement.
 44. Thedigital processing system as set forth in claim 25, said processorfurther configured to calculate said evaluation measurement of saidplurality of candidate decodings, wherein said evaluation measurement isa horizontal correlation measurement.
 45. The digital processing systemas set forth in claim 25, said processor further configured to calculatesaid evaluation measurement of said plurality of candidate decodings,wherein said evaluation measurement is a vertical correlationmeasurement.
 46. The digital processing system as set forth in claim 25,said processor further configured to calculate said evaluationmeasurement of saidplurality of candidate decodings, wherein saidevaluation measurement is a diagonal correlation measurement.
 47. Thedigital processing system as set forth in claim 25, said processorfurther configured to calculating said evaluation measurement of saidplurality of candidate decodings, wherein said evaluation measurement isa temporal activity measurement.
 48. The digital processing system ofclaim 25, wherein the specified parameters are selected from the groupconsisting of motion flag values and quantization bit values.
 49. Acomputer readable medium containing executable instructions which, whenexecuted in a processing system, cause the system to recover damageddata in a bitstream of encoded data, the instructions causing the systemto perform the following steps comprising: generating a plurality ofcandidate decodings based on specified parameters; calculating at leastone an evaluation measurement for each of said plurality of candidatedecodings, according to the specified parameters, each of saidevaluation measurements being determined based upon how a correspondingcandidate decoding fits in with other decoded data of the bitstream ofencoded data and according to the specified parameters; selecting acandidate decoding of the pulurality of candidate decodings; determiningat least one specific parameter for the candidate decoding; and decodingthe bitstream based upon the at least one specified parameter.
 50. Thecomputer readable medium as set forth in claim 49, further comprisingexecutable instruction which, when executed in a processing system,cause the system to further calculate said evaluation measurement ofeach of the plurality of candidate decoding by measuring motion of saidplurality of candidate decodings, determining a plurality of motiongroups by evaluating said motion of said plurality of candidatedecodings, and calculating an evaluation measurement of said pluralityof candidate decodings according to said plurality of motion groups. 51.The computer readable medium as set forth in claim 49, wherein saidencoded data is generated from a video signal, said video signalcomprising a plurality of video frames, said computer readable mediumfurther containing executable instructions which, when executed in theprocessing system, cause the system to further determine said pluralityof motion groups by measuring a spatial and temporal correlation of saidplurality of candidate decodings of said video frames, and determiningsaid plurality of motion groups by comparing a threshold to said spatialand temporal correlation.
 52. The computer readable medium as set forthin claim 49, wherein said encoded data is generated from a video signal,said video signal comprising a plurality of interlacing fields, saidcomputer readable medium further containing executable instructionswhich, when executed in the processing system, cause the system tofurther determine said plurality of motion groups by measuring a spatialand temporal correlation of said plurality of candidate decodings ofsaid interlacing fields, and determiningsaid plurality of motion groupsby comparing a threshold to said spatial and temporal correlation. 53.The computer readable medium as set forth in claim 49, wherein saidencoded data is generated from a video signal, said video signalcomprising a plurality of video frames, said computer readable mediumfurther containing executable instructions which, when executed in theprocessing system, cause the system to further determine said pluralityof motion groups by measuring a sum of changed samples of said pluralityof candidate decodings of said video frames and determining saidplurality of motion groups by comparing a threshold to said sum ofchanged samples.
 54. The computer readable medium as set forth in claim49, wherein said encoded data is generated from a video signal, saidvideo signal comprising a plurality of interlacing fields, said computerreadable medium further containing executable instructions which, whenexecuted in the processing system, cause the system to further determinesaid plurality of motion groups by measuring a sum of changed samples ofsaid plurality of candidate decodings of said interlacing fields anddetermining said plurality of motion groups by comparing a threshold tosaid sum of changed samples.
 55. The computer readable medium as setforth claim 49, wherein said encoded data is generated from a videosignal, said video signal comprising a plurality of video frames, saidcomputer readable medium further containing executable instructionswhich, when executed in the processing system, cause the system tofurther determine said plurality of motion groups by measuring a maximumabsolute value of changed samples of said plurality of candidatedecodings of said video frames and determining said plurality of motiongroups by comparing a threshold to said maximum absolute value ofchanged samples.
 56. The computer readable medium as set forth claim 49,wherein said encoded data is generated from a video signal, said videosignal comprising a plurality of interlacing fields, said computerreadable medium further containing executable instructions which, whenexecuted in the processing system, cause the system to further determinesaid plurality of motion groups by measuring a maximum absolute value ofchanged samplesof said plurality of candidate decodings of saidinterlacing fields and determining said plurality of motion groups bycomparing a threshold to said maximum absolute value of changed samples.57. The computer readable medium as set forth claim 49, containingexecutable instructions which, when executed in a processing system,cause the system to further calculate said evaluation measurement ofsaid plurality of candidate decodings by normalizing candidate decodedvalues of said plurality of candidate decodings, and calculating saidevaluation measurement of said plurality of candidate decodings.
 58. Thecomputer readable medium as set forth claim 49, containing executableinstructions which, when executed in a processing system, cause thesystem to further calculate said evaluation measurement of saidplurality of candidate decodings, wherein said evaluation measurement isa smoothness measurement.
 59. The computer readable medium as set forthin claim 58, containing executable instructions which, when executed ina processing system, cause the system to calculate said evaluationmeasurement of said plurality of candidate decodings by: normalizing{tilde over (x)}[i][j], where {tilde over (x)}[i][j] is the candidatedecoded value at location [i,j] in an area of the candidate decodedimage; and computing smoothness using the {tilde over (x)}[i][j],Laplacian kernel L[m][n], and a block Laplacian value L_(x), determinedaccording to the following:${{{l\lbrack i\rbrack}\quad\lbrack j\rbrack} = {\sum\limits_{m = {- 1}}^{1}{\sum\limits_{n = {- 1}}^{1}{{{L\lbrack m\rbrack}\lbrack n\rbrack}{{\overset{\sim}{x}\left\lbrack {i + m} \right\rbrack}\quad\left\lbrack {j + n} \right\rbrack}}}}},{0 \leq i},{j < K}$${L_{x} = \frac{\sum\limits_{i,j}{{{l\lbrack i\rbrack}\quad\lbrack j\rbrack}}}{K \cdot K}},{0 \leq i},{j < {K.}}$


60. The computer readable medium as set forth in claim 59, containingexecutable instructions which, when executed in a processing system,cause the system to normalize {tilde over (x)}[i][j] according to thefollowing:${{{\overset{\sim}{x}\lbrack i\rbrack}\quad\lbrack j\rbrack} = \frac{{x\lbrack i\rbrack}\lbrack j\rbrack}{\sqrt{\sum\limits_{i,j}\left( {{{x\lbrack i\rbrack}\lbrack j\rbrack} - X_{mean}} \right)^{2}}}},{0 \leq i},{j < K}$${where},{X_{mean} = \frac{\sum\limits_{i,j}{{x\lbrack i\rbrack}\lbrack j\rbrack}}{K \cdot K}},{0 \leq i},{j < {K.}}$


61. The computer readable medium as set forth in claim 60, where K is 8.62. The computer readable medium as set forth in claim 58, wherein thesmoothness measure comprises a second order image surface property. 63.The computer readable medium as set forth in claim 58, wherein thesmoothness measure comprises an N order image surface property, whereN>1.
 64. The computer readable medium as set forth in claim 58, whereinan image portion comprises a portion of a frame of data, said frameportion comprising portions of a first and second interlacing fields ofdata, said medium containing executable instructions which, whenexecuted in a processing system, cause the system to measure thesmoothness by: determining if the image portion includes motion; if theimage portion includes motion; computing a smoothness measurement for afirst field portion of the image portion and a second field portion ofthe image portion; and averaging the smoothness measurements together togenerate the smoothness measure; and if the image portion does notinclude motion, generating the smoothness measure for the frame portionof the image portion.
 65. The computer readable medium as set forthclaim 49, containing executable instructions which, when executed in aprocessing system, cause the system to further calculate said evaluationmeasurement of said plurality of candidate decodings, wherein saidevaluation measurement is a Laplacian measurement.
 66. The computerreadable medium as set forth claim 49, containing executableinstructions which, when executed in a processing system, causethesystem to further calculate said evaluation measurement of saidplurality of candidate decodings, wherein said evaluation measurement isa square error measurement.
 67. The computer readable medium is setforth claim 49, containing executable instructions which, when executedin a processing system, cause the system to further calculate saidevaluation measurement of said plurality of candidate decodings, whereinsaid evaluation measurement is a spatial correlation measurement. 68.The computer readable medium as set forth claim 49, containingexecutable instructions which, when executed in a processing system,cause the system to further calculate said evaluation measurement ofsaid plurality of candidate decodings, wherein said evaluationmeasurement is a horizontal correlation measurement.
 69. The computerreadable medium as set forth claim 49, containing executableinstructions which, when executed in a processing system, cause thesystem to further calculate said evaluation measurement of saidplurality of candidate decodings, wherein said evaluation measurement isa vertical correlation measurement.
 70. The computer readable medium asset forth claim 49, containing executable instruction which, whenexecuted in a processing system, cause the system to further calculatesaid evaluation measurement of said plurality of candidate decodings,wherein said evaluation measurement is a diagonal correlationmeasurement.
 71. The computer readable medium as set forth claim 49,containing executable instructions which, when executed in a processingsystem, cause the system to further calculate said evaluationmeasurement of said plurality of candidate decodings, wherein saidevaluation measurement is a temporal activity measurement.
 72. Thecomputer readable medium of claim 49, wherein the specified parametersare selected from the group consisting of motion flag values andquantization bit values.
 73. An apparatus for recovering damaged data ina bitstream of encoded data comprising: means for generating a pluralityof candidate decodings based on specified parameters; means forcalculating at least one an evaluation measurement for each of saidplurality of candidate decodings, according to the specified parameters,each of said evaluation measurements being determined based upon how acorresponding candidate decoding fits in with other decoded data of thebitstream of encoded data and according to the specified parameters;means for selecting a candidate decoding of the pulurality of candidatedecodings; means for determining at least one specific parameter for thecandidate decoding; and means for decoding the bitstream based upon theat least one specified parameter.
 74. The apparatus as set forth inclaim 73, wherein said means for calculating said evaluation measurementfurther comprises: means for measuring motion of said plurality ofcandidate decodings; means for determining a plurality of motion groupsby evaluating said motion of said plurality of candidate decodings; andmeans for calculating an evaluation measurement of said plurality ofcandidate decodings according to said plurality of motion groups. 75.The apparatus as set forth in claim 74, wherein said encoded data isgenerated from a video signal, said video signal comprising a pluralityof video frames, said means for determination of said plurality ofmotion groups further comprising: means for measuring a spatial andtemporal correlation of said plurality of candidate decodings of saidvideo frames; and means for determining said plurality of motion groupsby comparing a threshold to said spatial and temporal correlation. 76.The apparatus as set forth in claim 74, wherein said encoded data isgenerated from a video signal, said video signal comprising a pluralityof interlacing fields, said means for determination of said plurality ofmotion groups further comprising: means for measuring a spatial andtemporal correlation of said plurality of candidate decodings of saidinterlacing fields; and means for determining said plurality of motiongroups by comparing a threshold to said spatial and temporalcorrelation.
 77. The apparatus as set forth in claim 74, wherein saidencoded data is generated from a video signal, said video signalcomprising a plurality of video frames, said means for determination ofsaid plurality of motion groups further comprising: means for measuringa sum of changed samples of said plurality of candidate decoding of saidvideo frames; and means for determining said plurality of motion groupsby comparing a threshold to said sum of changed samples.
 78. Theapparatus as set forth in claim 74, wherein said encoded data isgenerated from a video signal, said video signal comprising a pluralityof interlacing fields, said means for determination of said plurality ofmotion groups further comprising: means for measuring a sum of changedsamples of said plurality of candidate decoding of said interlacingfields; and means for determining said plurality of motion groups bycomparing a threshold to said sum of changed samples.
 79. The apparatusas set forth in claim 74, wherein said encoded data is generated from avideo signal, said video signal comprising a plurality of video frames,said means for determination of said plurality of motion groups furthercomprising: means for measuring a maximum absolute value of changedsamples of said plurality of candidate decodings of said video frames;and means for determining said plurality of motion groups by comparing athreshold to said maximum absolute value of changed samples.
 80. Theapparatus as set forth in claim 74, wherein said encoded data isgenerated from a video signal, said video signal comprising a pluralityof interlacing fields, said means for determination of said plurality ofmotion groups further comprising: means for measuring a maximum absolutevalue of changed samples of said plurality of candidate decodings ofsaid interlacing fields; and means for determining said plurality ofmotion groups by comparing a threshold to said maximum absolute value ofchanged samples.
 81. The apparatus as set forth in claim 73, whereinsaid means for calculating said evaluation measurement furthercomprises: means for normalizing candidate decoded values of saidplurality of candidate decodings; and means for calculating saidevaluation measurement of said plurality of candidate decodings.
 82. Theapparatus as set forth in claim 73, wherein said evaluation measurementis a smoothness measurement.
 83. The apparatus as set forth in claim 79,wherein said means for calculating said evaluation measurement furthercomprises: means for normalizing {tilde over (x)}[i][j], where {tildeover (x)}[i][j] is the candidate decoded value at location [i,j] in anarea of the candidate decoded image; and means for computing smoothnessusing the normalized {tilde over (x)}[i][j], Laplacian kernel L[m][n],and a block Laplacian value L_(x), determined according to thefollowing:${{{l\lbrack i\rbrack}\quad\lbrack j\rbrack} = {\sum\limits_{m = {- 1}}^{1}{\sum\limits_{n = {- 1}}^{1}{{{L\lbrack m\rbrack}\lbrack n\rbrack}{{\overset{\sim}{x}\left\lbrack {i + m} \right\rbrack}\quad\left\lbrack {j + n} \right\rbrack}}}}},{0 \leq i},{j < K}$${L_{x} = \frac{\sum\limits_{i,j}{{{l\lbrack i\rbrack}\quad\lbrack j\rbrack}}}{K \cdot K}},{0 \leq i},{j < {K.}}$


84. The apparatus as set forth in claim 83, where the means fornormalizing computes according to the following equation:${{{\overset{\sim}{x}\lbrack i\rbrack}\quad\lbrack j\rbrack} = \frac{{x\lbrack i\rbrack}\lbrack j\rbrack}{\sqrt{\sum\limits_{i,j}\left( {{{x\lbrack i\rbrack}\lbrack j\rbrack} - X_{mean}} \right)^{2}}}},{0 \leq i},{j < K}$${where},{X_{mean} = \frac{\sum\limits_{i,j}{{x\lbrack i\rbrack}\lbrack j\rbrack}}{K \cdot K}},{0 \leq i},{j < {K.}}$


85. The apparatus as set forth in claim 84, wherein K is equal to
 8. 86.The apparatus as set forth in claim 82, wherein the smoothness measurecomprises a second order image surface property.
 87. The apparatus asset forth in claim 10, wherein the smoothness measure comprises an Norder image surface property, where N>1.
 88. The apparatus as set forthin claim 10, wherein an image portion comprises a portion of a frame ofdata, said frame portion comprising portions of first and secondinterlacing fields of data, the means for calculating said evaluationmeasurement further comprising: means for determining if the imageportion includes motion; if the image portion includes motion; means forcomputing a smoothness measurement for a first field portion of theimage portion and a second field portion of the image portion; and meansfor averaging the smoothness measurements together to generate thesmoothness measure; and if the image portion does not include motion,means for generating the smoothness measure for the frame portion of theimage portion.
 89. The apparatus as set forth in claim 73, wherein saidevaluation measurement is a Laplacian measurement.
 90. The apparatus asset forth in claim 73, wherein said evaluation measurement is a squareerror measurement.
 91. The apparatus as set forth in claim 73, whereinsaid evaluation measurement is a spatial correlation measurement. 92.The apparatus as set forth in claim 73, wherein said evaluationmeasurement is a horizontal correlation measurement.
 93. The apparatusas set forth in claim 73, wherein said evaluation measurement is avertical correlation measurement.
 94. The apparatus as set forth inclaim 73, wherein said evaluation measurement is a diagonal correlationmeasurement.
 95. The apparatus as set forth in claim 73, wherein saidevaluation measurement is a temporal activity measurement.
 96. Theapparatus of claim 73, wherein the specified parameters are selectedfrom the group consisting of motion flag values and quantization bitvalues.